Data Platform Engineer in London

Location: London or Newcastle with a minimum 2 days a week office attendance.

Contract Type: Permanent Full Time

Salary: London £70,000; Newcastle £61,250 plus civil service employer pension employer contribution of 28.9%.

Application Deadline: 5.00pm Sunday 5th July. First stage online interviews WC 6th July; final 2nd stage interviews on the 14th and 15th July.

Nationality Requirement:

• UK Nationals
• Nationals of Commonwealth countries who have the right to work in the UK
• Nationals from the EU, EEA or Switzerland with (or eligible for) status under the European Union Settlement Scheme (EUSS)

We will not provide sponsorship for work visas for this position. Applicants must already meet the nationality requirements outlined above.

About The National Audit Office:

The National Audit Office (NAO) is the UK’s main public sector audit body. Independent of government, we audit the accounts of various public sector bodies, examine the propriety of government spending, assess risks to financial control and accountability, and review the economy, efficiency and effectiveness of programmes, projects, and activities. We report directly to Parliament, through the Committee of Public Accounts of the House of Commons which uses our reports as the basis of its own investigations.

We employ approximately 1,000 people, most of whom are qualified accountants, trainees, or technicians. The organisation comprises two service lines: financial audit, and value for money (VFM) audit and has a strong core of highly talented corporate teams. We welcome applications from everyone, value diversity, support flexible working, and guarantee to interview all disabled applicants who meet the minimum criteria.

Introduction:

This is a new vacancy created within NAO’s Digital Services (DS) to expand the data team within the Audit Technology & Data pillar, with responsibility for designing, building, and maintaining the infrastructure that enables robust data ingestion process, storage, and access across the organization. This role supports the development and continual improvement of NAO data & technology service composition and provision, enabling scalable and reliable data solutions.

In this capacity, you will build and optimise data pipelines, integrate diverse data sources, and ensure the efficient movement of data across systems. You will work closely with analytics engineers, data scientists, and other stakeholders to ensure data is accessible, high‑quality, and fit for purpose. Your work will underpin the NAO’s ability to derive insights and automate processes using corporate and client data.

In This Role, You Will:

• Design, develop, and maintain scalable data pipelines and ETL processes.
• Integrate structured and unstructured data from internal and external sources.
• Ensure data quality, consistency, and security across systems aligning with the NAO’s data strategy.
• Collaborate with analytics engineers and subject matter experts to support data modelling and transformation.
• Work closely with other digital roles including Cybersecurity, BI, Architecture to ensure effective delivery.
• Monitor and optimise performance of data infrastructure.
• Test, monitor, and document data architecture and engineering processes to ensure transparency and maintainability.

This role reports into the Audit Data Platform Lead and requires regular attendance at the NAO’s office either in Victoria, London, or at the office in Newcastle.

Key Responsibilities:

As a data engineer at the NAO, you will play a critical role in building and maintaining the technical foundation that enables data‑driven operations and insights. You will be responsible for architecting and managing data infrastructure, ensuring that data flows securely and efficiently across systems, and enabling downstream users to access reliable, well‑structured data. You will take ownership of the design and delivery of scalable cloud data pipelines, with a focus on Microsoft Azure‑based solutions.

Your Key Responsibilities Will Include:

• Building scalable data infrastructure: Design and implement systems that support the ingestion, storage, and processing of large volumes of structured and unstructured data from internal and external sources.
• Developing robust data pipelines: Create automated workflows that extract, transform, and load data into centralized platforms, ensuring consistency, reliability, and performance across all stages.
• Designing and optimising ETL processes: Build and maintain efficient ETL (Extract, Transform, Load) workflows to move data from source systems into usable formats. Ensure these processes are scalable, well‑documented, and aligned with data quality standards.
• Integrating diverse data sources: Connect and harmonise data from various systems (e.g., operational databases, APIs, cloud services) to create unified datasets for analysis and reporting.
• Collaborating across teams: Work closely with analytics engineers, data scientists, and business stakeholders to understand data needs and deliver infrastructure that supports analytical and operational use cases.
• Ensuring data reliability and performance: Monitor data systems for latency, failures, and bottlenecks. Implement performance tuning and system optimisations to maintain high availability and responsiveness.
• Implementing data governance and security protocols: Apply best practices for data privacy, access control, and compliance. Ensure that sensitive data is protected and handled in accordance with regulatory requirements.
• Maintaining technical documentation: Produce and update documentation for data architecture, pipeline configurations, and operational procedures to support transparency and continuity.
• Troubleshooting and incident response: Investigate and resolve data‑related issues, from pipeline failures to data integrity concerns. Establish proactive monitoring and alerting systems.
• Supporting data accessibility: Enable self‑service access to clean, well‑organised data for analysts and other users through tools, APIs, or data platforms.
• Keeping pace with technology: Stay informed about emerging tools, frameworks, and methodologies in data engineering. Continuously evaluate and adopt innovations that improve efficiency and scalability.

Key Skills / Competencies Required:

• Communicating between the technical and non‑technical (Skill level: Awareness)
• Data Analysis and Synthesis (Skill level: Working)
• Data Development Process (Skill level: Working)
• Data Innovation (Skill level: Awareness)
• Data Integration Design (Skill level: Working)
• Data Modelling (Skill level: Working)
• Metadata Management (Skill level: Working)
• Problem Management (Skill level: Awareness)
• Programming and Build (Data Engineering) (Skill level: Working)
• Technical Understanding (Skill level: Working)
• Testing (Skill level: Working)

You can explain why it is important to communicate technical concepts in non‑technical language. You understand the types of communication used with internal and external stakeholders and their impact.

You can undertake data profiling and source system analysis. You present clear insights to colleagues to support the end use of the data.

You can design, build, and test data products based on feeds from multiple systems, using a range of storage technologies and access methods. You create repeatable and reusable products.

You show awareness of opportunities for innovation with new tools and uses of data.

You deliver data solutions in accordance with agreed organisational standards that ensure services are resilient, scalable, and future‑proof.

You understand the concepts and principles of data modelling. You can produce, maintain, and update relevant data models and reverse‑engineer models from live systems.

You use metadata repositories to complete complex tasks such as data and systems integration impact analysis. You maintain metadata repositories to ensure accuracy and currency.

You investigate problems in systems, processes, and services, and contribute to the implementation of remedies and preventative measures.

You can design, code, test, correct, and document simple programs or scripts under direction. You follow agreed standards and tools.

You understand core technical concepts related to the role and apply them with guidance.

You review requirements and specifications, define test conditions, identify issues and risks, and report test activities and results.

Essential Criteria:

• Deep, hands‑on experience as a cloud‑based Data Engineer, ideally within Microsoft Azure environments.
• Expert‑level experience designing and delivering ETL/ELT pipelines at scale.
• Strong experience in data modelling, including standardisation, best practice, and semantic layer design.
• Advanced Python skills for data processing, optimisation, and automation.
• Strong SQL expertise, including T‑SQL and PostgreSQL.
• Proven experience implementing and operating medallion architecture patterns.
• Experience with cloud‑native Azure data services, including:
• Azure Databricks
• Microsoft Fabric
• Azure Data Factory
• Demonstrable experience in pipeline performance tuning and optimisation.
• Experience working closely with DevOps engineers, with a solid understanding of CI/CD, Infrastructure as Code (IaC) such as Terraform.
• Knowledge in Software Development Life Cycle (SDLC) practices, including automated testing (e.g. Pytest).

Desirable / Preferred Experience:

• Experience in analytics engineering, including development of semantic data models for reporting and analytics.
• Experience with infrastructure and data governance tooling, such as:
• Power BI
• Microsoft Purview
• Azure Databricks
• Experience in cluster management, parallel computing.
• Experience using industry‑standard ETL, orchestration, and scheduling tools, including:
• Azure Data Factory
• Azure Automation
• Azure DevOps
• Ability to leverage multiple programming and scripting languages, including Python and PowerShell, to automate platform operations and engineering workflows.

Experience Requirements:

• ETL and Data Pipeline Development – Demonstrated cloud‑based experience in designing, building, and maintaining ETL workflows and deploying data pipelines. Skilled in extracting, transforming, and loading data from various sources into centralized platforms.
• Data Infrastructure and Integration – Proven ability to implement data flows between operational systems and analytics platforms. Experience with cloud‑based data services (Azure preferred) and streaming systems is desirable.
• Database Management and Optimization – Experience managing relational and non‑relational databases, including performance tuning, indexing, and query optimisation. Familiarity with database design principles and data warehousing solutions.
• Collaboration and Communication – Ability to work effectively with technical and non‑technical stakeholders. Skilled in translating business requirements into technical solutions and supporting cross‑functional teams.
• Problem Solving and Troubleshooting – Capable of identifying and resolving data‑related issues, implementing preventative measures, and contributing to system reliability.

How to Apply:

Please upload a current CV and a covering letter before the deadline, clearly outlining your knowledge and experience against the experience requirements above, including experience utilizing Terraform.