Job description Analytics Engineer
Contract type: Permanent
Location: London
Salary: c£65,000 per annum plus civil service pension employer contribution of 28%. Higher salary ranges may be available for exceptional candidates.
Hours: Flexible working and part time hours will be considered. The NAO offers hybrid working based on a minimum of 2 days a week in the office.
Closing date for applications is 23:59pm on Sunday 23 February 2025.
Nationality Requirement:
1. UK Nationals
2. Nationals of Commonwealth countries who have the right to work in the UK
3. Nationals from the EU, EEA or Switzerland with (or eligible for) status under the European Union Settlement Scheme (EUSS)
Please note, we are not able to sponsor work visas or accept temporary visas as we are looking to hire on a permanent basis. Please contact the HR Service desk (hrservicedesk@nao.org.uk) 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 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.
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:
The analytics engineer is a newly created role within the NAO’s Digital Services (DS) function with responsibility for supporting the development and continual improvement of NAO data & technology service composition and provision. They will support emerging tech to enable the automation or acceleration of relevant NAO processes and derive deeper insights from corporate and client data.
In this capacity, you will transform organizational data into structured formats suitable for analysis and decision-making. You will develop and test data models, explore local data sources, and construct pipelines from corporate repositories to data science and machine learning models. Acting as a domain-specific collaborator to data engineers, you will facilitate the conversion of data into actionable intelligence, thereby contributing to the NAO's commitment to data-driven excellence.
In this role, you will:
1. Collaborate with subject matter experts and data users to design optimized data structures and models for analysis.
2. Support data quality improvement and develop standards for data transformation.
3. Create, maintain, and document data processes to ensure transparency and usability.
4. Refine requirements based on user feedback and organizational changes.
5. Provide ongoing support, training, and issue resolution for data users.
6. Ensure data documentation meets established standards.
Responsibilities of the role:
As an analytics engineer, you are responsible for ensuring data is clean, structured, and ready for analysis. You will create data models, automate data processes, and collaborate with stakeholders to support business decisions. Your work makes it easier for us to derive insights from data.
In this role, your responsibilities will include:
1. Design, build, and maintain data pipelines: Develop and manage robust data pipelines that ensure efficient and reliable data flow from various sources to data storage and processing systems.
2. Develop and optimize ETL processes: Create and enhance ETL (Extract, Transform, Load) processes to extract data from multiple sources, transform it into a usable format, and load it into data storage systems.
3. Collaborate with data scientists and analysts: Work closely with data scientists, analysts, and other stakeholders to understand their data requirements.
4. Implement and manage data warehousing solutions: Design, implement, and maintain data warehousing solutions that support scalable and efficient data storage and retrieval.
5. Ensure data governance and security: Implement best practices for data governance, including data privacy, security, and compliance with relevant regulations.
6. Optimize database performance: Manage and optimize relational and non-relational databases to ensure efficient data storage, retrieval, and performance.
7. Create and maintain documentation: Develop comprehensive documentation for data pipelines, ETL processes, and data architecture.
8. Monitor and troubleshoot data systems: Proactively monitor data systems for issues, perform root cause analysis, and implement solutions.
9. Support data-driven decision-making: Provide the data infrastructure and tools that enable stakeholders to access and analyze data effectively.
10. Stay updated with industry trends: Continuously learn and apply new technologies, tools, and best practices in data engineering and analytics.
11. Develop and maintain data documentation: Create and update comprehensive documentation for all data-related processes.
Key skills / competencies required:
1. Communicating between the technical and non-technical (Skill level: Practitioner)
2. Data Analysis and Synthesis (Skill level: Working)
3. Data Innovation (Skill level: Working)
4. Data Modelling, Cleansing and Enrichment (Skill level: Practitioner)
5. Metadata Management (Skill level: Practitioner)
6. Problem Management (Skill level: Working)
7. Programming and Build (data engineering) (Skill level: Practitioner)
8. Testing (Skill level: Working)
9. Turning business problems into design (Skill level: Practitioner)
Experience:
1. Strong proficiency in data analysis and statistical methods.
2. Experience in data engineering and ETL processes.
3. Proficiency in database management and optimization.
4. Strong problem-solving and communication skills.
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