Ready to help shape the future of digital archiving? Apply now and join a team where your work matters – for today, and for generations to come.

Application Process

• Interview: Interviews will be held on-site, ideally week commencing 2nd November 2026. The interview will include a technical test.
• Personal Statement: We ask all applicants to upload their CV and to submit a personal statement of 600-700 words total.

Selection for interview will be based on the 3 ‘essential’ criteria listed below so please ensure that your application demonstrates in detail how you meet these requirements.

• Data manipulation: Strong experience of accurate, reliable, large-scale analysis and manipulation of complex datasets using relevant programming languages and tools (e.g. Shell scripting, SQL or XSLT).
• Database technologies: Practical knowledge of database technologies (e.g. RDBMS, noSQL, graph databases or linked data stores).
• Analysis and problem solving: Excellent analytical skills, with a structured and proactive approach to solving technical problems. Able to apply a range of techniques to capture, document, and communicate requirements, issues, designs, and solutions.

Applicants invited to interview will be assessed against ALL of the essential criteria (see job description below).

SC clearance/willingness to obtain SC clearance will be required for this role. This requires candidates to have been resident in the UK for at least the past five years. Please do not apply if you have been resident in the UK for less than five years as your application will be rejected.

Artificial Intelligence can be a useful tool to support your application, however, all examples and statements provided must be truthful, factually accurate, and taken directly from your own experience. Where plagiarism has been identified (presenting the ideas and experiences of others, or generated by artificial intelligence, as your own) applications may be withdrawn and internal candidates may be subject to disciplinary action. Please visit the Civil Service Careers website where you can find further information on the use of AI in the application guidance section.

Job Description

Department: Digital Archiving
Reports to: Lead Data Engineer
Band: Band E – (Civil Service Equivalent – HEO)
Location: Kew (Hybrid working available)
Line manages: Junior staff, suppliers, and contractors as required

Job purpose

Archives are the homes of our collective memory, past and future. The National Archives is the archive of UK government, and one of the world’s leading digital archives. As the scale and complexity of our digital collections continue to grow, we ensure our data is robust, connected, and accessible.

We are looking for a Data Engineer who thrives on solving complex problems, working at scale, and creating the pipelines and models that power access to the nation’s records. You will play a key role in transforming how archival data is managed, structured, and enriched, helping to unlock its value for public use and discovery.

This role is ideal for someone who enjoys designing and building efficient data workflows, developing tools to automate data processing, and collaborating with product teams and specialists to understand data needs and deliver practical solutions. You will bring strong programming skills and a good understanding of data modelling, transformation, and integration across a variety of formats and technologies.

You will work in a supportive, forward-looking team that values transparency, curiosity, and collaboration. You’ll have opportunities to develop your skills in existing and emerging technologies that enhance public access to archives and promote the re-use of data in meaningful ways.

Role and responsibilities

This role falls under the GDD (formerly DDaT) Data Engineer and Data Analyst roles with elements of the Data Scientist role.

Key Responsibilities

Data processing and pipelines

• Design and develop scalable, repeatable data pipelines (ETL/ELT) to process, transform, and load archival data.
• Create scripts and tools for data cleansing, validation, standardisation, enrichment, and transformation.
• Automate routine data tasks to improve efficiency, accuracy, and consistency.
• Identify and manage connections between datasets, including those external to TNA.
• Research and implement tools for entity extraction and semantic tagging to improve the usability and findability of archival data.

Data modelling and integration

• Develop and document schemas, ontologies, and other models that represent and structure archival data.
• Connect and integrate data from different internal and external sources, including structured and semi-structured formats (such as XML, CSV, JSON, or RDF).
• Identify patterns and relationships within data to support better discovery and reuse.

Technical advice and collaboration

• Develop excellent relationships with product teams, advocate for data-centric approaches to software development, and help solve data problems.
• Maintain effective communication with external technical partners (e.g. suppliers), representing The National Archives to resolve complex data issues, clarify requirements, and ensure smooth delivery of digital archiving workflows.
• Work closely with product teams, archivists, and developers to support data-centric solutions.
• Provide advice and input on data engineering approaches within the team.
• Share knowledge on data standards, validation rules, and transformation practices.

Quality, openness, and growth

• Work in the open and share code and documentation internally (and externally where appropriate).
• Participate in code reviews, technical discussions, and knowledge sharing.
• Contribute to building a data culture focused on reuse, quality, and continuous improvement.

Working conditions

• Normal office environment
• Display Screen Equipment user
• Hybrid working pattern, 60% of time on-site, possibly more when new to the role

Person specification

Essential skills and experience:

• Data manipulation: Strong experience of accurate, reliable, large-scale analysis and manipulation of complex datasets using relevant programming languages and tools (e.g. Shell scripting, SQL, or XSLT).
• Database technologies: Practical knowledge of database technologies (e.g. RDBMS, noSQL, graph databases, or linked data stores).
• Analysis and problem solving: Excellent analytical skills, with a structured and proactive approach to solving technical problems. Able to apply a range of techniques to capture, document, and communicate requirements, issues, designs, and solutions.
• Communication and relationships: Strong relationship building and communication skills, with an excellent user