Data Platform Engineer

UK National Audit Office; Newcastle upon Tyne; On-site; GBP 61,000 - 70,000; Full time

Job summary

UK National Audit Office is seeking a Data Engineer to design, build, and maintain scalable data pipelines and ETL processes within the Data Platform. You will integrate structured and unstructured data from internal and external sources, ensuring data quality and security, while collaborating with analytics engineers, data scientists, and business stakeholders.

This role requires regular office attendance in London or Newcastle.

Qualifications

• Experience building scalable data pipelines and ETL/ELT workflows.
• Strong cloud experience in Microsoft Azure data services.
• Proficient in data modeling and semantic layer design.

Responsibilities

• Design, develop, and maintain scalable data pipelines and ETL processes.
• Integrate diverse data sources for unified datasets.
• Collaborate with analytics engineers and data scientists to support data needs.
• Ensure data quality, security, and performance across systems.
• Maintain documentation for data architecture and pipelines.

Skills

Azure

ETL pipelines

Python

SQL

Data modeling

Tools

PowerShell

CI/CD

Job description

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

Contract Type: Permanent Full Time

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

The deadline for applications is 11.59 pm Sunday 23rd July. We will be holding interviews WC 31st Aug.

Nationality Requirement:

• 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)

Please be aware that we do not provide sponsorship for work visas for this position. Applicants must already meet the nationality requirements outlined above. If you have any questions regarding your eligibility, please contact the HR Service desk ([e-mail]) should you have any questions on your nationality eligibility.

About the National Audit Office

The National Audit Office (NAO) is the UK’s main public sector audit body. Independent of government, we have responsibility for auditing the accounts of various public sector bodies, examining the propriety of government spending, assessing risks to financial control and accountability, and reviewing 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 approx. 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.

The NAO welcomes applications from everyone. We value diversity in all its forms and the difference it makes to our organisation. By removing barriers and creating an inclusive culture all our people can develop and maximise their full potential. As members of the Business Disability Forum and the Disability Confident Scheme we guarantee to interview all disabled applicants who meet the minimum criteria.

The NAO supports flexible working and is happy to discuss this with you at application stage.

Context and main purpose of the job:

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 optimize 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 the other digital roles including Cybersecurity, BI, Architecture to ensure effective delivery.
• Monitor and optimize 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

This role requires regular attendance at the NAO’s office either in Victoria, London, or at the office in Newcastle.

Responsibilities of the role:

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 optimizing 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 harmonize 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 optimizations 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

The skill sets listed also include the corresponding skill level (awareness, working, practitioner, expert):

• Communicating between the technical and non-technical (Skill level: Awareness)

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

• Data Analysis and Synthesis (Skill level: Working)

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

• 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 Azure data services, including:
• Microsoft Fabric
• 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 terraform
• Knowledge in Software Development Life Cycle (SDLC) practices, including automated testing (e.g. Pytest).
• Experience in analytics engineering, including development of semantic data models for reporting and analytics.
• Experience with infrastructure and data governance tooling, such:
• Experience in cluster management, parallel computing
• Experience using industry-standard ETL, orchestration, and scheduling tools, including:
• 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 optimization. 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.