Build systems that collect, manage, and convert data into usable information for data scientists, data analysts and business intelligence analysts to interpret.
This occupation is found in a wide range of public and private sector organisations who work with large data sets including Government departments, NHS, financial and professional services, IT companies, retail and sales and education providers.
The purpose of the occupation is to build systems that collect, manage, and convert data into usable information for data scientists, data analysts and business intelligence analysts to interpret. A data engineer’s main aim is to make data accessible and valid so that an organisation can use it to evaluate and optimise their performance. The role of the data engineer is pivotal to any organisation; it ensures that data pipelines are established to support data scientists and other business stakeholders.
A data engineer will build and implement data flows to connect operational systems, and re-engineer manual data flows to enable scalable and repeatable use. They integrate, support and manage the build of data streaming systems, writing extract transform and load scripts that perform in line with business requirements.
They are responsible for providing high quality, transparent data that enables effective governance and smart business decisions. They will analyse the performance indicators of the data systems that provide clean, regular, and accurate data. A data engineer will understand how data and an organisation’s data architecture is essential to business outcomes.
A data engineer will be able to gather requirements for data solutions, and they demonstrate and articulate data solutions to stakeholders in a way that can be easily understood. Data engineering encompasses a range of activities from collecting data to employing various data processing frameworks, including but not limited to ETL (Extract, Transform, Load), and collaborating with data scientists and other data-centric roles. Data engineers may work in an office or work remotely depending on the sector they work in and location of the employer.
In their daily work, an employee in this occupation will work autonomously or collaboratively with clients, in the business and or data team. A data engineer will work with data analysts, Data scientists and data architects and liaise with other teams and internal and external stakeholders to ensure their data requirements are captured and managed to the specified standard. They will also work closely with machine learning engineer (Ops), software engineers, software developers and technology teams.
An employee in this occupation will be responsible for completing their own work to specification, , ensuring they meet set deadlines. A data engineer contributes towards, engineering designs, plans, execution and evaluation working to time, cost and quality targets. They deliver to the product roadmap and are responsible for meeting quality requirements and working in accordance with health and safety and environmental considerations. They will work according to organisational procedures and policies, to maintain security and compliance.
This is a summary of the key things that you – the apprentice and your employer need to know about your end-point assessment (EPA). You and your employer should read the EPA plan for the full details. It has information on assessment method requirements, roles and responsibilities, and re-sits and re-takes.
An EPA is an assessment at the end of your apprenticeship. It will assess you against the knowledge, skills, and behaviours (KSBs) in the occupational standard. Your training will cover the KSBs. The EPA is your opportunity to show an independent assessor how well you can carry out the occupation you have been trained for.
Your employer will choose an end-point assessment organisation (EPAO) to deliver the EPA. Your employer and training provider should tell you what to expect and how to prepare for your EPA.
The length of the training for this apprenticeship is typically 24 months. The EPA period is typically 4 months.
The overall grades available for this apprenticeship are:
When you pass the EPA, you will be awarded your apprenticeship certificate.
The EPA gateway is when the EPAO checks and confirms that you have met any requirements required before you start the EPA. You will only enter the gateway when your employer says you are ready.
The gateway requirements for your EPA are:
Project with report
You will complete a project and write a report. You will be asked to complete a project. The title and scope must be agreed with the EPAO at the gateway. The report should be a maximum of 3500 words (with a 10% tolerance).
You will have 10 weeks to complete the project and submit the report to the EPAO.
You need to prepare and give a presentation to an independent assessor. Your presentation slides and any supporting materials should be submitted at the same time as the project output. The presentation with questions will last at least 50 minutes. The independent assessor will ask at least 6 questions about the project and presentation.
Professional discussion
You will have a professional discussion with an independent assessor. It will last 80 minutes. They will ask you at least 10 questions. The questions will be about certain aspects of your occupation. You can use it to help answer the questions.
The EPAO will confirm where and when each assessment method will take place.
You should speak to your employer if you have a query that relates to your job.
You should speak to your training provider if you have any questions about your training or EPA before it starts.
You should receive detailed information and support from the EPAO before the EPA starts. You should speak to them if you have any questions about your EPA once it has started.
If you have a disability, a physical or mental health condition or other special considerations, you may be able to have a reasonable adjustment that takes this into account. You should speak to your employer, training provider and EPAO and ask them what support you can get. The EPAO will decide if an adjustment is appropriate.
This occupation is found in a wide range of public and private sector organisations who work with large data sets including Government departments, NHS, financial and professional services, IT companies, retail and sales and education providers.
The purpose of the occupation is to build systems that collect, manage, and convert data into usable information for data scientists, data analysts and business intelligence analysts to interpret. A data engineer’s main aim is to make data accessible and valid so that an organisation can use it to evaluate and optimise their performance. The role of the data engineer is pivotal to any organisation; it ensures that data pipelines are established to support data scientists and other business stakeholders.
A data engineer will build and implement data flows to connect operational systems, and re-engineer manual data flows to enable scalable and repeatable use. They integrate, support and manage the build of data streaming systems, writing extract transform and load scripts that perform in line with business requirements.
They are responsible for providing high quality, transparent data that enables effective governance and smart business decisions. They will analyse the performance indicators of the data systems that provide clean, regular, and accurate data. A data engineer will understand how data and an organisation’s data architecture is essential to business outcomes.
A data engineer will be able to gather requirements for data solutions, and they demonstrate and articulate data solutions to stakeholders in a way that can be easily understood. Data engineering encompasses a range of activities from collecting data to employing various data processing frameworks, including but not limited to ETL (Extract, Transform, Load), and collaborating with data scientists and other data-centric roles. Data engineers may work in an office or work remotely depending on the sector they work in and location of the employer.
In their daily work, an employee in this occupation will work autonomously or collaboratively with clients, in the business and or data team. A data engineer will work with data analysts, Data scientists and data architects and liaise with other teams and internal and external stakeholders to ensure their data requirements are captured and managed to the specified standard. They will also work closely with machine learning engineer (Ops), software engineers, software developers and technology teams.
An employee in this occupation will be responsible for completing their own work to specification, , ensuring they meet set deadlines. A data engineer contributes towards, engineering designs, plans, execution and evaluation working to time, cost and quality targets. They deliver to the product roadmap and are responsible for meeting quality requirements and working in accordance with health and safety and environmental considerations. They will work according to organisational procedures and policies, to maintain security and compliance.
Duty | KSBs |
---|---|
Duty 1 Build and optimise automated data systems and pipelines considering data quality, description, cataloguing, data cleaning, validation, technical documentation and requirements. |
|
Duty 2 Integrate, support and manage data using standalone, distributed and cloud-based platforms. To ensure efficient, sustainable and effective provision of data storage solutions. |
|
Duty 3 Support the identification and evaluation of opportunities for data acquisition and data enrichment. |
|
Duty 4 Select and use appropriate tools to process data in any format, such as structured and unstructured data and in any mode of delivery, such as streaming or batching. Adapt to legacy systems as required. |
|
Duty 5 Ensure resilience is built into data products against business continuity and disaster recovery plans, and document change management to limit service outages. Support and respond to incidents through the application of technology and service management best practice including configuration, change and incident management. |
|
Duty 6 Analyse requirements, research scope and options and present recommendations for solutions to stakeholders. |
|
Duty 7 Support the implementation of prototype or proof-of-concept data products within a production environment |
|
Duty 8 Maintain data solutions as continually evolving products, to service the organisation, user or client requirements. Collaborate with technical support teams and stakeholders from implementation to management. |
|
Duty 9 Working within compliance and contribute towards data governance, organisational policies, standards, and guidelines for data engineering. |
|
Duty 10 Monitor the data system to meet service requirements to enable solutions such as data analysis, dashboards, data products, pipelines, and storage solutions. |
|
Duty 11 Keep up to date with engineering developments to advance own skills and knowledge. |
K1: Processes to monitor and optimise the performance of the availability, management and performance of data product.
Back to Duty
K2: Methodologies for moving data from one system to another for storage and further handling.
Back to Duty
K3: Data normalisation principles and the advantages they achieve in databases for data protection, redundancy, and inconsistent dependency.
Back to Duty
K4: Frameworks for data quality, covering dimensions such as accuracy, completeness, consistency, timeliness, and accessibility.
Back to Duty
K5: The inherent risks of data such as incomplete data, ethical data sources and how to ensure data quality.
Back to Duty
K6: Software development principles for data products, including debugging, version control, and testing.
Back to Duty
K7: Principles of sustainable data products and organisational responsibilities for environmental social governance.
Back to Duty
K8: Deployment approaches for new data pipelines and automated processes.
Back to Duty
K9: How to build a data product that complies with regulatory requirements.
Back to Duty
K10: Concepts of data governance, including regulatory requirements, data privacy, security, and quality control. Legislation and its application to the safe use of data.
Back to Duty
K11: Data and information security standards, ethical practices, policies and procedures relevant to data management activities such as data lineage and metadata management.
Back to Duty
K12: How to cost and build a system whilst ensuring that organisational strategies for sustainable, net zero technologies are considered.
Back to Duty
K13: The implications of financial, strategic and compliance regarding to security, scalability, compliance and cost of local, remote or distributed solutions.
Back to Duty
K14: The uses of on-demand Cloud computing platform(s) in a public or private environment such as Amazon AWS, Google Cloud, Hadoop, IBM Cloud, Salesforce and Microsoft Azure.
Back to Duty
K15: Data warehousing principles, including techniques such as star schemas, data lakes, and data marts.
Back to Duty
K16: Principles of data, including open and public data, administrative data, and research data including the value of external data sources that can be used to enrich internal data. Examples of how business use direct data acquisition to support or augment business operations.
Back to Duty
K17: Approaches to data integration and how combining disparate data sources delivers value to an organisation.
Back to Duty
K18: How to use streaming, batching and on-demand services to move data from one location to another.
Back to Duty
K19: Differences between structured, semi-structured, and unstructured data.
Back to Duty
K20: Types and uses of data engineering tools and applications in own organisation.
Back to Duty
K21: Policies and strategies to ensure business continuity for operations, particularly in relation to data provision.
Back to Duty
K22: Technology and service management best practice including configuration, change and incident management.
Back to Duty
K23: How to undertake analysis and root cause investigation.
Back to Duty
K24: Processes for evaluating prototypes and taking them to implementation within a production environment.
Back to Duty
K25: The lifecycle of implementing data solutions in a business, from scoping, though prototyping, development, production, and continuous improvement.
Back to Duty
K26: Data development frameworks and approved organisational architectures.
Back to Duty
K27: The principles of descriptive, predictive and prescriptive analytics.
Back to Duty
K28: Continuous improvement including how to: capture good practice and lessons learned.
Back to Duty
K29: Strategies for keeping up to date with new ways of working and technological developments in data science, data engineering and AI.
Back to Duty
K30: The methods and techniques used to communicate messages to meet the needs of the audience.
Back to Duty
S1: Collate, evaluate and refine user requirements to design the data product.
Back to Duty
S2: Collate, evaluate and refine business requirements including cost, resourcing, and accessibility to design the data product.
Back to Duty
S3: Design a data product to serve multiple needs and with scalability, efficiency, and security in mind.
Back to Duty
S4: Automate data pipelines such as batch, real-time, on demand and other processes using programming languages and data integration platforms with graphical user interfaces.
Back to Duty
S5: Produce and maintain technical documentation explaining the data product, that meets organisational, technical and non-technical user requirements, retaining critical information.
Back to Duty
S6: Systematically clean, validate, and describe data at all stages of extract, transform, load (ETL).
Back to Duty
S7: Work with different types of data stores, such as SQL, NoSQL, and distributed file system.
Back to Duty
S8: Identify and troubleshoot issues with data processing pipelines.
Back to Duty
S9: Query and manipulate data using tools and programming such as SQL and Python. Manage database access, and implement automated validation checks.
Back to Duty
S10: Communicate downtime and issues with database access to stakeholders to mitigate the operational impact of unforeseen issues.
Back to Duty
S11: Evaluate opportunities to extract value from existing data products through further development, considering costs, environmental impact and potential operational benefits.
Back to Duty
S12: Maintain a working knowledge of data use cases within organisations.
Back to Duty
S13: Use data systems securely to meet requirements and in line with organisational procedures and legislation.
Back to Duty
S14: Identify new tools and technologies and recommend potential opportunities for use in own department or organisation.
Back to Duty
S15: Optimise data ingestion processes by making use of appropriate data ingestion frameworks such as batch, streaming and on-demand.
Back to Duty
S16: Develop algorithms and processes to extract structured data from unstructured sources.
Back to Duty
S17: Apply and advocate for software development best practice when working with other data professionals throughout the business. Contribute to standards and ways of working that support software development principles.
Back to Duty
S18: Develop simple forecasts and monitoring tools to anticipate or respond immediately to outages and incidents.
Back to Duty
S19: Identify and escalate risks with suggested mitigation/resolutions as appropriate.
Back to Duty
S20: Investigate and respond to incidents, identifying the root cause and resolution with internal and external stakeholders.
Back to Duty
S21: Identify and remediate technical debt, assess for updates and obsolescence as part of continuous improvement.
Back to Duty
S22: Develop, maintain collaborative relationships using adaptive business methodology with stakeholders such as, business users, data scientists, data analysts and business intelligence teams.
Back to Duty
S23: Present, communicate, and disseminate messages about the data product, tailoring the message and medium to the needs of the audience.
Back to Duty
S24: Evaluate the strengths and weaknesses of prototype data products and how these integrate within an organisation’s overarching data infrastructure.
Back to Duty
S25: Assess and identify gaps in existing tools and technologies in respect of implementing changes required.
Back to Duty
S26: Identify data quality metrics and track them to ensure the quality, accuracy and reliability of the data product.
Back to Duty
S27: Selects and apply sustainable solutions to contribute to net zero and environmental strategies across the various stages of product and service delivery.
Back to Duty
S28: Horizon scanning to identify new technologies that offer increased performance of data products.
Back to Duty
S29: Implement personal strategies to keep up to date with new technology and ways of working.
Back to Duty
B1: Acts proactively and takes accountability adapting positively to changing work priorities, ensuring deadlines are met.
Back to Duty
B2: Works collaboratively with stakeholders and colleagues, developing strong working relationships to achieve common goals. Support an inclusive culture and treat technical and non- technical colleagues and stakeholders with respect.
Back to Duty
B3: Quality focus that promotes continuous improvement utilising peer review techniques, innovation and creativity to the data system development process to improve processes and address business challenges.
Back to Duty
B4: Takes personal responsibility towards net zero and prioritises environmental sustainability outcomes in how they carry out the duties of their role.
Back to Duty
B5: Use initiative and innovation to problem solve and trouble shoot, providing creative solutions.
Back to Duty
B6: Keeps abreast of developments in emerging, contemporary and advanced technologies to optimise sustainable data products and services.
Back to Duty
Apprentices without level 2 English and maths will need to achieve this level prior to taking the End-Point Assessment. For those with an education, health and care plan or a legacy statement, the apprenticeship’s English and maths minimum requirement is Entry Level 3. A British Sign Language (BSL) qualification is an alternative to the English qualification for those whose primary language is BSL.
V1.0
This document explains the requirements for end-point assessment (EPA) for the data engineer apprenticeship. End-point assessment organisations (EPAOs) must follow this when designing and delivering the EPA.
Data engineer apprentices, their employers and training providers should read this document.
A full-time data engineer apprentice typically spends 24 months on-programme (this means in training before the gateway). The apprentice must spend at least 12 months on-programme and complete the required amount of off-the-job training in line with the apprenticeship funding rules.
The apprentice must complete their training and meet the gateway requirements before starting their EPA. The EPA will assess occupational competence.
An approved EPAO must conduct the EPA for this apprenticeship. Employers must select an approved EPAO from the register of end-point assessment organisations (RoEPAO).
This EPA has 2 assessment methods.
The grades available for each assessment method are below.
Assessment method 1 - project evaluation report, presentation and questions:
Assessment method 2 - professional discussion:
The result from each assessment method is combined to decide the overall apprenticeship grade. The following grades are available for the apprenticeship:
On-programme - typically 24 months
|
The apprentice must:
|
---|---|
End-point assessment gateway
|
The apprentice’s employer must be content that the apprentice has attained sufficient KSBs to complete the apprenticeship. The apprentice must:
For the project evaluation report, presentation and questions, the apprentice must submit a project brief. To ensure the project allows the apprentice to meet the KSBs mapped to this assessment method to the highest available grade, the EPAO should sign-off the project’s title and scope at the gateway to confirm it is suitable.
The apprentice must submit the gateway evidence to their EPAO, including any organisation specific policies and procedures requested by the EPAO. |
End-point assessment - typically 4 months
|
The grades available for each assessment method are below
Project evaluation report, presentation and questions:
Professional discussion:
Overall EPA and apprenticeship can be graded:
|
The EPA is taken in the EPA period. The EPA period starts when the EPAO confirms the gateway requirements have been met and is typically 4 months.
The EPAO should confirm the gateway requirements have been met and start the EPA as quickly as possible.
The apprentice’s employer must be content that the apprentice has attained sufficient KSBs to complete the apprenticeship. The employer may take advice from the apprentice's training provider, but the employer must make the decision. The apprentice will then enter the gateway.
The apprentice must meet the gateway requirements before starting their EPA.
They must:
The apprentice must submit the gateway evidence to their EPAO, including any organisation specific policies and procedures requested by the EPAO.
The assessment methods can be delivered in any order.
The result of one assessment method does not need to be known before starting the next.
A project involves the apprentice completing a significant and defined piece of work that has a real business application and benefit. The project must meet the needs of the employer’s business and be relevant to the apprentice’s occupation and apprenticeship.
The agreed project will present a typical business task, appropriate for demonstrating the skills and knowledge on the standard. The agreed project will be comparable in terms of content and complexity for all apprentices - it is the context within which the knowledge, and skills must be demonstrated that will vary. The project is undertaken and completed on programme and pre-gateway to the EPA. The project itself is not part of the EPA. The project will typically be undertaken on the employer’s premises.
This assessment method has 2 components:
project with a project output
presentation with questions and answers
Together, these components give the apprentice the opportunity to demonstrate the KSBs mapped to this assessment method. They are assessed by an independent assessor.
This assessment method is being used because:
A project evaluation report is the most valid method as it allows the demonstration of professional competence.
The project is based on a real life example of the apprentices’ everyday work in their industry. Therefore, ensuring that they can demonstrate the KSBs in practice.
Producing a project evaluation report and presentation reflects normal professional practice, so this assessment method is appropriate.
Apprentices are required to be concise and precise in their use of language in written and verbal communication.
The project evaluation report offers a realistic opportunity to combine project management, examples of data products and formal writing enabling the apprentice to reflect on approaches taken.
It is a holistic assessment method, allowing the apprentice to demonstrate KSBs in an integrated way.
By writing the evaluation report on the project and being questioned to understand rationale for choices made, risks and problems identified, resolutions and areas where further action could be required. This method will enable the apprentice to showcase their professional competency.
The project is completed before gateway and is not graded. The project evaluation report is assessed and must be completed after gateway.
The apprentice must complete a project based on any of the following:
To ensure the project allows the apprentice to meet the KSBs mapped to this assessment method to the highest available grade, the EPAO must sign-off the project’s title and scope at the gateway to confirm it is suitable. The EPAO must refer to the grading descriptors to ensure that projects are pitched appropriately.
The project output must be in the form of a report and presentation.
The apprentice must start the project before gateway. The project evaluation report must be completed after gateway. The employer should ensure the apprentice has the time and resources, to plan and complete their project.
The apprentice may work as part of a team to complete the project, which could include internal colleagues or technical experts. The apprentice must however, complete their project report and presentation unaided and they must be reflective of their own role and contribution. The apprentice and their employer must confirm this when the report and any presentation materials are submitted.
The report must include at least:
The report must also include:
To ensure the project allows the apprentice to meet the KSBs mapped to this assessment method to the highest available grade, the EPAO should sign-off the project evaluation report's title and scope at the gateway to confirm it is suitable.
The project output must be in the form of an evaluation report.
The apprentice must start the project evaluation report after the gateway. They must complete and submit the report to the EPAO by the end of week 10 of the EPA period.
The employer should ensure the apprentice has the time and resources, within this period, to plan and complete their project evaluation report. The apprentice must complete their project evaluation report and the production of its components unaided.
The apprentice may work as part of a team to complete the project which could include technical internal or external support. However, the project evaluation report must be the apprentice’s own work and reflective of their own role and contribution. The apprentice and their employer must confirm that the project evaluation report is the apprentice’s own work when it is submitted.
The project report must have a word count of 3500 words. A tolerance of 10% above or below is allowed at the apprentice’s discretion. Appendices, references and diagrams are not included in this total. The apprentice must produce and include a mapping in an appendix, showing how the report evidences the KSBs mapped to this assessment method.
The apprentice must complete and submit the report and any presentation materials to the EPAO by the end of week 10 of the EPA period.
The presentation with questions must be structured to give the apprentice the opportunity to demonstrate the KSBs mapped to this assessment method to the highest available grade.
The apprentice must prepare and deliver a presentation to an independent assessor. After the presentation, the independent assessor must ask the apprentice questions about their project, report and presentation.
The presentation should cover:
The presentation with questions must last 50 minutes. This will typically include a presentation of 20 minutes and questioning lasting 30 minutes. The independent assessor must use the full time available for questioning. The independent assessor can increase the time of the presentation and questioning by up to 10%. This time is to allow the apprentice to complete their last point or respond to a question if necessary.
The independent assessor must ask at least 6 questions. They must use the questions from the EPAO’s question bank or create their own questions in line with the EPAO’s training. Follow up questions are allowed where clarification is required.
The purpose of the independent assessor's questions is:
The apprentice must submit any presentation materials to the EPAO at the same time as the report - by the end of week 10 of the EPA period. The apprentice must notify the EPAO, at that point, of any technical requirements for the presentation.
During the presentation, the apprentice must have access to:
The independent assessor must have at least 2 weeks to review the project report and any presentation materials, to allow them to prepare questions.
The apprentice must be given at least 2 weeks’ notice of the presentation with questions.
The independent assessor must make the grading decision. They must assess the project components holistically when deciding the grade.
The independent assessor must keep accurate records of the assessment. They must record:
The presentation with questions must take place in a suitable venue selected by the EPAO for example, the EPAO’s or employer’s premises. It should take place in a quiet room, free from distractions and influence.
The presentation with questions can be conducted by video conferencing. The EPAO must have processes in place to verify the identity of the apprentice and ensure the apprentice is not being aided.
The EPAO must develop a purpose-built assessment specification and question bank. It is recommended this is done in consultation with employers of this occupation. The EPAO must maintain the security and confidentiality of EPA materials when consulting with employers. The assessment specification and question bank must be reviewed at least once a year to ensure they remain fit-for-purpose.
The assessment specification must be relevant to the occupation and demonstrate how to assess the KSBs mapped to this assessment method. The EPAO must ensure that questions are refined and developed to a high standard. The questions must be unpredictable. A question bank of sufficient size will support this.
The EPAO must ensure that the apprentice has a different set of questions in the case of re-sits or re-takes.
EPAO must produce the following materials to support the project:
The EPAO must ensure that the EPA materials are subject to quality assurance procedures including standardisation and moderation.
In the professional discussion, an independent assessor and apprentice have a formal two-way conversation. It gives the apprentice the opportunity to demonstrate the KSBs mapped to this assessment method.
This assessment method is being used because:
It provides the apprentice with the opportunity to discuss and show case their depth of understanding the knowledge, skills and behaviours that may not naturally occur as part of the project.
It allows the independent assessor to consider the context and sector that the apprentice operates within, giving flexibility to ensure that all the KSBs can be assessed appropriately.
The professional discussion is cost effective, and it allows consideration of the potential need to conduct the EPA remotely.
The professional discussion must be structured to give the apprentice the opportunity to demonstrate the KSBs mapped to this assessment method to the highest available grade.
An independent assessor must conduct and assess the professional discussion.
The apprentice may choose to end the assessment method early. The apprentice must be confident they have demonstrated competence against the assessment requirements for the assessment method. The independent assessor or EPAO must ensure the apprentice is fully aware of all assessment requirements. The independent assessor or EPAO cannot suggest or choose to end any assessment methods early unless in an emergency. The EPAO is responsible for ensuring the apprentice understands the implications of ending an assessment early if they choose to do so. The independent assessor may suggest the assessment continues. The independent assessor must document the apprentice’s request to end the assessment early.
The EPAO will ask ten questions, two for each of the themes:
The EPAO must give an apprentice 2 weeks' notice of the professional discussion.
The professional discussion must last for 80 minutes. The independent assessor can increase the time of the professional discussion by up to 10%. This time is to allow the apprentice to respond to a question if necessary.
The independent assessor must ask at least 10 questions. The independent assessor must use the questions from the EPAO’s question bank or create their own questions in line with the EPAO’s training. Follow-up questions are allowed where clarification is required.
The independent assessor must make the grading decision.
The independent assessor must keep accurate records of the assessment. They must record:
The professional discussion must take place in a suitable venue selected by the EPAO for example, the EPAO’s or employer’s premises.
The professional discussion can be conducted by video conferencing. The EPAO must have processes in place to verify the identity of the apprentice and ensure the apprentice is not being aided.
The professional discussion should take place in a quiet room, free from distractions and influence.
The EPAO must develop a purpose-built assessment specification and question bank. It is recommended this is done in consultation with employers of this occupation. The EPAO must maintain the security and confidentiality of EPA materials when consulting with employers. The assessment specification and question bank must be reviewed at least once a year to ensure they remain fit-for-purpose.
The assessment specification must be relevant to the occupation and demonstrate how to assess the KSBs mapped to this assessment method. The EPAO must ensure that questions are refined and developed to a high standard. The questions must be unpredictable. A question bank of sufficient size will support this.
The EPAO must ensure that the apprentice has a different set of questions in the case of re-sits or re-takes.
The EPAO must produce the following materials to support the professional discussion:
The EPAO must ensure that the EPA materials are subject to quality assurance procedures including standardisation and moderation.
Theme
KSBs
|
Pass
Apprentices must demonstrate all of the pass descriptors
|
Distinction
Apprentices must demonstrate all of the pass descriptors and all of the distinction descriptors
|
---|---|---|
Data product design
K6 K7 K9 K12 K13 K14 S1 S2 S3 S4 S5 S27 B1 |
Demonstrates how they have collated, evaluated and refined user requirements to design and build a scalable data product that serves multiple needs and complies with regulatory requirements. (K9, S1, S3) Explains how they collated, evaluated and refined business requirements, to design, build and maintain a system whilst ensuring that organisational strategies for sustainable, net-zero technologies are considered. (K12 & S2) Explains how they selected sustainable solutions in relation to data products and environmental social governance to ensure the use of less carbon across the various stages of product and service delivery. (K7, S27) Demonstrates how they used security, scalability and governance when automating data pipelines using programming languages and data integration platforms with graphical user interfaces. (K13, S4) Demonstrates how they have taken accountability produced and maintained technical documentation for a data product in order to meet organisational user requirements, whilst adapting to changing work priorities to ensure that deadlines are met. (S5, B1) Explains how debugging, version control and testing have an impact on software development and the principles for data products. (K6) Outlines the uses of different on-demand cloud computing platforms. (K14) |
Justifies how the data product created met the requirements and served multiple needs (S1, S3) |
Data product deployment and evaluation
K2 K4 K8 K15 K17 K19 K20 K24 K25 K26 S6 S9 S16 S24 S26 |
Explains the deployment approaches processes for new data pipelines and automated processes.(K8) Explains techniques such as star schemas, data lakes and data marts and the impact they have on data warehousing principles. (K15) Demonstrate how to systematically clean, validate and describe data at all stages of extract, transform and load, showing how combining disparate data sources and taking different approaches to data integration delivers value to an organisation. (K17, S6) Describes the types and uses of data engineering tools in their own organisation and how they apply them. (K20) Evaluates the strengths and weaknesses of prototype data products to integrate within an organisation’s overarching data structure, taking into consideration the lifecycle of implementing data solutions in a business. (K24, K25, S24) Describes the approved organisational architectures and the relevant data development frameworks. (K26) Identifies data quality metrics and their frameworks and tracks them to ensure quality, accuracy and reliability of the data product. (K4, S26) Demonstrates the use of tools and programming to query and manipulate data and implement automated validation checks, showing the methodologies used for moving data from one system to another for storage and handling. (K2, S9) Explains how they have worked with structured, semi-structured and unstructured data, developing algorithms to extract from sources (K19, S16) |
Evaluates the success of the algorithm developed (S16) |
Collaborative working
K30 S22 S23 B2 |
Outlines the methods and techniques used to communicate messages about the data product that meet the needs of the audience. (K30, S23) Explains how they worked collaboratively with different technical and non-technical stakeholders, using adaptive business methodology to support an inclusive culture and develop and maintain strong working relationships in order to achieve common goals. (S22, B2) |
Evaluate the impact of the methods and techniques used to communicate messages about the data product to the audience. (K30, S23) |
Theme
KSBs
|
Pass
Apprentices must demonstrate all of the pass descriptors
|
Distinction
Apprentices must demonstrate all of the pass descriptors and all of the distinction descriptors
|
---|---|---|
Data quality and performance
K1 K3 K5 K18 K27 S7 S15 |
Explains how they monitor different types of data store to optimise system management, performance and availability. (K1, S7) Defines data normalisation principles and the advantages that they achieve for data protection, redundancy and inconsistent dependency. (K3) Explains the inherent risks of data and how to ensure data quality (K5) Explains the principles of descriptive, predictive and prescriptive analytics. (K27) Describes how they use data ingestion frameworks such as streaming, batching and on demand services to move data from one location to another in order to optimise data ingestion processes. (K18, S15) |
Compares and contrasts the different types of data stores they have used and how they optimised performance (K1, S7) |
Problem Solving
K21 K22 K23 S8 S10 S12 S18 S19 S20 B5 |
Describes technology and service management best practice. (K22) Explains how they identify and escalate risks and incidents, communicating downtime and issues with database access in line with policies in order to mitigate operational impact whilst ensuring business continuity. (K21, S10, S18, S19) Explains how they have maintained a working knowledge of data use cases within organisations. (S12) Explains how their analysis of root cause investigation is used to respond to incidents within data processing pipelines, whilst troubleshooting and providing resolutions to stakeholders. (K23, S8, S20, B5)
|
Justifies the approach taken to manage risks and incidents to maintain business continuity. (S18, S19) |
Regulatory Compliance
K10 K11 S13 |
Explains their use of data, information security standards, ethical practices and data management policies and procedures to ensure data systems are used securely and in accordance with relevant legislation. (K11, S13) Explains the legislative associated with the use and collation of data, including concepts of data governance and regulatory requirements. (K10) |
None |
Continuous Improvement
K16 K28 S11 S14 S17 S21 S25 S28 B3 B4 |
Outlines how they evaluate opportunities to extract value from existing data products whilst applying the principles of data and considering costs, environmental impact and potential operating benefits. (K16, S11) Explains how they take personal responsibility within the duties of their role to identify new tools and technologies, and recommend potential opportunities for use in own department or organisation in order to prioritise environmental sustainability outcomes to work towards net zero. (S14, B4)
Explains how they take a quality focussed approach to identify and remediate technical debt and assess for updates and obsolescence within their promotion of continuous improvement, by utilising peer review techniques and capturing good practice, to provide innovation and creativity to the data system development process in order to improve processes and address business challenges. (K28, S21, B3) Explains how they apply ways of working that support software development principles and advocate software development best practice when working with other data professionals. (S17) Explains how they identify and assess new technologies, as well as gaps in existing tools and technologies, that offer increased performance of data products and implementation of changes required. (S25, S28) |
Evaluates the impact that the implementation of identified new technologies would have on practices within the organisation. (S25, S28) |
Continuous professional development
K29 S29 B6 |
Explains how they have implemented personal strategies for keeping up to date with new ways of working and to keep abreast of developments in emerging, contemporary and advanced technologies, in order to keep up to date with new technologies and technological developments in data science, data engineering and AI and to optimise sustainable products and services (K29, S29, B6) |
Evaluate the impact that keeping up to date with technological developments has had on their own professional development. (S29) |
Performance in the EPA determines the overall grade of:
An independent assessor must individually grade the project evaluation report, presentation and questions and professional discussion in line with this EPA plan.
The EPAO must combine the individual assessment method grades to determine the overall EPA grade.
If the apprentice fails one assessment method or more, they will be awarded an overall fail.
To achieve an overall pass, the apprentice must achieve at least a pass in all the assessment methods. Both assessment methods are weighted equally in their contribution to the overall EPA grade.
Grades from individual assessment methods must be combined in the following way to determine the grade of the EPA overall.
Project evaluation report, presentation and questions | Professional discussion | Overall Grading |
---|---|---|
Fail | Fail | Fail |
Fail | Pass | Fail |
Pass | Fail | Fail |
Pass | Pass | Pass |
Pass | Distinction | Merit |
Distinction | Pass | Merit |
Distinction | Distinction | Distinction |
If the apprentice fails one assessment method or more, they can take a re-sit or a re-take at their employer’s discretion. The apprentice’s employer needs to agree that a re-sit or re-take is appropriate. A re-sit does not need further learning, whereas a re-take does. The apprentice should have a supportive action plan to prepare for a re-sit or a re-take.
The employer and the EPAO should agree the timescale for a re-sit or re-take. A re-sit is typically taken within 3 months of the EPA outcome notification. The timescale for a re-take is dependent on how much re-training is required and is typically taken within 6 months of the EPA outcome notification.
If the apprentice fails the project assessment method, they must amend the project output in line with the independent assessor’s feedback. The apprentice will be given 4 weeks to rework and submit the amended report.
Failed assessment methods must be re-sat or re-taken within a 6-month period from the EPA outcome notification, otherwise the entire EPA will need to be re-sat or re-taken in full.
Re-sits and re-takes are not offered to an apprentice wishing to move from pass to a higher grade.
The apprentice will get a maximum EPA grade of pass for a re-sit or re-take, unless the EPAO determines there are exceptional circumstances.
Roles | Responsibilities |
---|---|
Apprentice |
As a minimum, the apprentice should:
|
Employer |
As a minimum, the apprentice's employer must:
|
EPAO |
As a minimum, the EPAO must:
|
Independent assessor |
As a minimum, an independent assessor must:
|
Training provider |
As a minimum, the training provider must:
|
The EPAO must have reasonable adjustments arrangements for the EPA.
This should include:
Adjustments must maintain the validity, reliability and integrity of the EPA as outlined in this EPA plan.
Internal quality assurance refers to the strategies, policies and procedures that an EPAO must have in place to ensure valid, consistent and reliable EPA decisions.
EPAOs for this EPA must adhere to the requirements within the roles and responsibilities table.
They must also appoint independent assessors who:
Affordability of the EPA will be aided by using at least some of the following:
This apprenticeship is not aligned to professional recognition.
Knowledge | Assessment methods |
---|---|
K1
Processes to monitor and optimise the performance of the availability, management and performance of data product. Back to Grading |
Professional discussion |
K2
Methodologies for moving data from one system to another for storage and further handling. Back to Grading |
Project evaluation report, presentation and questions |
K3
Data normalisation principles and the advantages they achieve in databases for data protection, redundancy, and inconsistent dependency. Back to Grading |
Professional discussion |
K4
Frameworks for data quality, covering dimensions such as accuracy, completeness, consistency, timeliness, and accessibility. Back to Grading |
Project evaluation report, presentation and questions |
K5
The inherent risks of data such as incomplete data, ethical data sources and how to ensure data quality. Back to Grading |
Professional discussion |
K6
Software development principles for data products, including debugging, version control, and testing. Back to Grading |
Project evaluation report, presentation and questions |
K7
Principles of sustainable data products and organisational responsibilities for environmental social governance. Back to Grading |
Project evaluation report, presentation and questions |
K8
Deployment approaches for new data pipelines and automated processes. Back to Grading |
Project evaluation report, presentation and questions |
K9
How to build a data product that complies with regulatory requirements. Back to Grading |
Project evaluation report, presentation and questions |
K10
Concepts of data governance, including regulatory requirements, data privacy, security, and quality control. Legislation and its application to the safe use of data. Back to Grading |
Professional discussion |
K11
Data and information security standards, ethical practices, policies and procedures relevant to data management activities such as data lineage and metadata management. Back to Grading |
Professional discussion |
K12
How to cost and build a system whilst ensuring that organisational strategies for sustainable, net zero technologies are considered. Back to Grading |
Project evaluation report, presentation and questions |
K13
The implications of financial, strategic and compliance regarding to security, scalability, compliance and cost of local, remote or distributed solutions. Back to Grading |
Project evaluation report, presentation and questions |
K14
The uses of on-demand Cloud computing platform(s) in a public or private environment such as Amazon AWS, Google Cloud, Hadoop, IBM Cloud, Salesforce and Microsoft Azure. Back to Grading |
Project evaluation report, presentation and questions |
K15
Data warehousing principles, including techniques such as star schemas, data lakes, and data marts. Back to Grading |
Project evaluation report, presentation and questions |
K16
Principles of data, including open and public data, administrative data, and research data including the value of external data sources that can be used to enrich internal data. Examples of how business use direct data acquisition to support or augment business operations. Back to Grading |
Professional discussion |
K17
Approaches to data integration and how combining disparate data sources delivers value to an organisation. Back to Grading |
Project evaluation report, presentation and questions |
K18
How to use streaming, batching and on-demand services to move data from one location to another. Back to Grading |
Professional discussion |
K19
Differences between structured, semi-structured, and unstructured data. Back to Grading |
Project evaluation report, presentation and questions |
K20
Types and uses of data engineering tools and applications in own organisation. Back to Grading |
Project evaluation report, presentation and questions |
K21
Policies and strategies to ensure business continuity for operations, particularly in relation to data provision. Back to Grading |
Professional discussion |
K22
Technology and service management best practice including configuration, change and incident management. Back to Grading |
Professional discussion |
K23
How to undertake analysis and root cause investigation. Back to Grading |
Professional discussion |
K24
Processes for evaluating prototypes and taking them to implementation within a production environment. Back to Grading |
Project evaluation report, presentation and questions |
K25
The lifecycle of implementing data solutions in a business, from scoping, though prototyping, development, production, and continuous improvement. Back to Grading |
Project evaluation report, presentation and questions |
K26
Data development frameworks and approved organisational architectures. Back to Grading |
Project evaluation report, presentation and questions |
K27
The principles of descriptive, predictive and prescriptive analytics. Back to Grading |
Professional discussion |
K28
Continuous improvement including how to: capture good practice and lessons learned. Back to Grading |
Professional discussion |
K29
Strategies for keeping up to date with new ways of working and technological developments in data science, data engineering and AI. Back to Grading |
Professional discussion |
K30
The methods and techniques used to communicate messages to meet the needs of the audience. Back to Grading |
Project evaluation report, presentation and questions |
Skill | Assessment methods |
---|---|
S1
Collate, evaluate and refine user requirements to design the data product. Back to Grading |
Project evaluation report, presentation and questions |
S2
Collate, evaluate and refine business requirements including cost, resourcing, and accessibility to design the data product. Back to Grading |
Project evaluation report, presentation and questions |
S3
Design a data product to serve multiple needs and with scalability, efficiency, and security in mind. Back to Grading |
Project evaluation report, presentation and questions |
S4
Automate data pipelines such as batch, real-time, on demand and other processes using programming languages and data integration platforms with graphical user interfaces. Back to Grading |
Project evaluation report, presentation and questions |
S5
Produce and maintain technical documentation explaining the data product, that meets organisational, technical and non-technical user requirements, retaining critical information. Back to Grading |
Project evaluation report, presentation and questions |
S6
Systematically clean, validate, and describe data at all stages of extract, transform, load (ETL). Back to Grading |
Project evaluation report, presentation and questions |
S7
Work with different types of data stores, such as SQL, NoSQL, and distributed file system. Back to Grading |
Professional discussion |
S8
Identify and troubleshoot issues with data processing pipelines. Back to Grading |
Professional discussion |
S9
Query and manipulate data using tools and programming such as SQL and Python. Manage database access, and implement automated validation checks. Back to Grading |
Project evaluation report, presentation and questions |
S10
Communicate downtime and issues with database access to stakeholders to mitigate the operational impact of unforeseen issues. Back to Grading |
Professional discussion |
S11
Evaluate opportunities to extract value from existing data products through further development, considering costs, environmental impact and potential operational benefits. Back to Grading |
Professional discussion |
S12
Maintain a working knowledge of data use cases within organisations. Back to Grading |
Professional discussion |
S13
Use data systems securely to meet requirements and in line with organisational procedures and legislation. Back to Grading |
Professional discussion |
S14
Identify new tools and technologies and recommend potential opportunities for use in own department or organisation. Back to Grading |
Professional discussion |
S15
Optimise data ingestion processes by making use of appropriate data ingestion frameworks such as batch, streaming and on-demand. Back to Grading |
Professional discussion |
S16
Develop algorithms and processes to extract structured data from unstructured sources. Back to Grading |
Project evaluation report, presentation and questions |
S17
Apply and advocate for software development best practice when working with other data professionals throughout the business. Contribute to standards and ways of working that support software development principles. Back to Grading |
Professional discussion |
S18
Develop simple forecasts and monitoring tools to anticipate or respond immediately to outages and incidents. Back to Grading |
Professional discussion |
S19
Identify and escalate risks with suggested mitigation/resolutions as appropriate. Back to Grading |
Professional discussion |
S20
Investigate and respond to incidents, identifying the root cause and resolution with internal and external stakeholders. Back to Grading |
Professional discussion |
S21
Identify and remediate technical debt, assess for updates and obsolescence as part of continuous improvement. Back to Grading |
Professional discussion |
S22
Develop, maintain collaborative relationships using adaptive business methodology with stakeholders such as, business users, data scientists, data analysts and business intelligence teams. Back to Grading |
Project evaluation report, presentation and questions |
S23
Present, communicate, and disseminate messages about the data product, tailoring the message and medium to the needs of the audience. Back to Grading |
Project evaluation report, presentation and questions |
S24
Evaluate the strengths and weaknesses of prototype data products and how these integrate within an organisation’s overarching data infrastructure. Back to Grading |
Project evaluation report, presentation and questions |
S25
Assess and identify gaps in existing tools and technologies in respect of implementing changes required. Back to Grading |
Professional discussion |
S26
Identify data quality metrics and track them to ensure the quality, accuracy and reliability of the data product. Back to Grading |
Project evaluation report, presentation and questions |
S27
Selects and apply sustainable solutions to contribute to net zero and environmental strategies across the various stages of product and service delivery. Back to Grading |
Project evaluation report, presentation and questions |
S28
Horizon scanning to identify new technologies that offer increased performance of data products. Back to Grading |
Professional discussion |
S29
Implement personal strategies to keep up to date with new technology and ways of working. Back to Grading |
Professional discussion |
Behaviour | Assessment methods |
---|---|
B1
Acts proactively and takes accountability adapting positively to changing work priorities, ensuring deadlines are met. Back to Grading |
Project evaluation report, presentation and questions |
B2
Works collaboratively with stakeholders and colleagues, developing strong working relationships to achieve common goals. Support an inclusive culture and treat technical and non- technical colleagues and stakeholders with respect. Back to Grading |
Project evaluation report, presentation and questions |
B3
Quality focus that promotes continuous improvement utilising peer review techniques, innovation and creativity to the data system development process to improve processes and address business challenges. Back to Grading |
Professional discussion |
B4
Takes personal responsibility towards net zero and prioritises environmental sustainability outcomes in how they carry out the duties of their role. Back to Grading |
Professional discussion |
B5
Use initiative and innovation to problem solve and trouble shoot, providing creative solutions. Back to Grading |
Professional discussion |
B6
Keeps abreast of developments in emerging, contemporary and advanced technologies to optimise sustainable data products and services. Back to Grading |
Professional discussion |
KSBS GROUPED BY THEME | Knowledge | Skills | Behaviour |
---|---|---|---|
Data product design
K6 K7 K9 K12 K13 K14 S1 S2 S3 S4 S5 S27 B1 |
Software development principles for data products, including debugging, version control, and testing. (K6) Principles of sustainable data products and organisational responsibilities for environmental social governance. (K7) How to build a data product that complies with regulatory requirements. (K9) How to cost and build a system whilst ensuring that organisational strategies for sustainable, net zero technologies are considered. (K12) The implications of financial, strategic and compliance regarding to security, scalability, compliance and cost of local, remote or distributed solutions. (K13) The uses of on-demand Cloud computing platform(s) in a public or private environment such as Amazon AWS, Google Cloud, Hadoop, IBM Cloud, Salesforce and Microsoft Azure. (K14) |
Collate, evaluate and refine user requirements to design the data product. (S1) Collate, evaluate and refine business requirements including cost, resourcing, and accessibility to design the data product. (S2) Design a data product to serve multiple needs and with scalability, efficiency, and security in mind. (S3) Automate data pipelines such as batch, real-time, on demand and other processes using programming languages and data integration platforms with graphical user interfaces. (S4) Produce and maintain technical documentation explaining the data product, that meets organisational, technical and non-technical user requirements, retaining critical information. (S5) Selects and apply sustainable solutions to contribute to net zero and environmental strategies across the various stages of product and service delivery. (S27) |
Acts proactively and takes accountability adapting positively to changing work priorities, ensuring deadlines are met. (B1) |
Data product deployment and evaluation
K2 K4 K8 K15 K17 K19 K20 K24 K25 K26 S6 S9 S16 S24 S26 |
Methodologies for moving data from one system to another for storage and further handling. (K2) Frameworks for data quality, covering dimensions such as accuracy, completeness, consistency, timeliness, and accessibility. (K4) Deployment approaches for new data pipelines and automated processes. (K8) Data warehousing principles, including techniques such as star schemas, data lakes, and data marts. (K15) Approaches to data integration and how combining disparate data sources delivers value to an organisation. (K17) Differences between structured, semi-structured, and unstructured data. (K19) Types and uses of data engineering tools and applications in own organisation. (K20) Processes for evaluating prototypes and taking them to implementation within a production environment. (K24) The lifecycle of implementing data solutions in a business, from scoping, though prototyping, development, production, and continuous improvement. (K25) Data development frameworks and approved organisational architectures. (K26) |
Systematically clean, validate, and describe data at all stages of extract, transform, load (ETL). (S6) Query and manipulate data using tools and programming such as SQL and Python. Manage database access, and implement automated validation checks. (S9) Develop algorithms and processes to extract structured data from unstructured sources. (S16) Evaluate the strengths and weaknesses of prototype data products and how these integrate within an organisation’s overarching data infrastructure. (S24) Identify data quality metrics and track them to ensure the quality, accuracy and reliability of the data product. (S26) |
None |
Collaborative working
K30 S22 S23 B2 |
The methods and techniques used to communicate messages to meet the needs of the audience. (K30) |
Develop, maintain collaborative relationships using adaptive business methodology with stakeholders such as, business users, data scientists, data analysts and business intelligence teams. (S22) Present, communicate, and disseminate messages about the data product, tailoring the message and medium to the needs of the audience. (S23) |
Works collaboratively with stakeholders and colleagues, developing strong working relationships to achieve common goals. Support an inclusive culture and treat technical and non- technical colleagues and stakeholders with respect. (B2) |
KSBS GROUPED BY THEME | Knowledge | Skills | Behaviour |
---|---|---|---|
Data quality and performance
K1 K3 K5 K18 K27 S7 S15 |
Processes to monitor and optimise the performance of the availability, management and performance of data product. (K1) Data normalisation principles and the advantages they achieve in databases for data protection, redundancy, and inconsistent dependency. (K3) The inherent risks of data such as incomplete data, ethical data sources and how to ensure data quality. (K5) How to use streaming, batching and on-demand services to move data from one location to another. (K18) The principles of descriptive, predictive and prescriptive analytics. (K27) |
Work with different types of data stores, such as SQL, NoSQL, and distributed file system. (S7) Optimise data ingestion processes by making use of appropriate data ingestion frameworks such as batch, streaming and on-demand. (S15) |
None |
Problem Solving
K21 K22 K23 S8 S10 S12 S18 S19 S20 B5 |
Policies and strategies to ensure business continuity for operations, particularly in relation to data provision. (K21) Technology and service management best practice including configuration, change and incident management. (K22) How to undertake analysis and root cause investigation. (K23) |
Identify and troubleshoot issues with data processing pipelines. (S8) Communicate downtime and issues with database access to stakeholders to mitigate the operational impact of unforeseen issues. (S10) Maintain a working knowledge of data use cases within organisations. (S12) Develop simple forecasts and monitoring tools to anticipate or respond immediately to outages and incidents. (S18) Identify and escalate risks with suggested mitigation/resolutions as appropriate. (S19) Investigate and respond to incidents, identifying the root cause and resolution with internal and external stakeholders. (S20) |
Use initiative and innovation to problem solve and trouble shoot, providing creative solutions. (B5) |
Regulatory Compliance
K10 K11 S13 |
Concepts of data governance, including regulatory requirements, data privacy, security, and quality control. Legislation and its application to the safe use of data. (K10) Data and information security standards, ethical practices, policies and procedures relevant to data management activities such as data lineage and metadata management. (K11) |
Use data systems securely to meet requirements and in line with organisational procedures and legislation. (S13) |
None |
Continuous Improvement
K16 K28 S11 S14 S17 S21 S25 S28 B3 B4 |
Principles of data, including open and public data, administrative data, and research data including the value of external data sources that can be used to enrich internal data. Examples of how business use direct data acquisition to support or augment business operations. (K16) Continuous improvement including how to: capture good practice and lessons learned. (K28) |
Evaluate opportunities to extract value from existing data products through further development, considering costs, environmental impact and potential operational benefits. (S11) Identify new tools and technologies and recommend potential opportunities for use in own department or organisation. (S14) Apply and advocate for software development best practice when working with other data professionals throughout the business. Contribute to standards and ways of working that support software development principles. (S17) Identify and remediate technical debt, assess for updates and obsolescence as part of continuous improvement. (S21) Assess and identify gaps in existing tools and technologies in respect of implementing changes required. (S25) Horizon scanning to identify new technologies that offer increased performance of data products. (S28) |
Quality focus that promotes continuous improvement utilising peer review techniques, innovation and creativity to the data system development process to improve processes and address business challenges. (B3) Takes personal responsibility towards net zero and prioritises environmental sustainability outcomes in how they carry out the duties of their role. (B4) |
Continuous professional development
K29 S29 B6 |
Strategies for keeping up to date with new ways of working and technological developments in data science, data engineering and AI. (K29) |
Implement personal strategies to keep up to date with new technology and ways of working. (S29) |
Keeps abreast of developments in emerging, contemporary and advanced technologies to optimise sustainable data products and services. (B6) |
Version | Change detail | Earliest start date | Latest start date | Latest end date |
---|---|---|---|---|
1.0 | Approved for delivery | 11/12/2023 | Not set | Not set |
Crown copyright © 2024. You may re-use this information (not including logos) free of charge in any format or medium, under the terms of the Open Government Licence. Visit www.nationalarchives.gov.uk/doc/open-government-licence