[What the role is]

MAS is expanding its EconTech capability within the Enterprise Knowledge Department to support the Economic Policy Group (EPG). EconTech is a team of economists who apply econometrics, data science and Artificial Intelligence to novel and large datasets in order to address policy questions across macroeconomic surveillance, inflation, trade and external demand, the labour market, and financial stability.

You will be the team's data engineer, responsible for building and running the data foundations that support EconTech and the wider EPG. You will help implement EPG's data strategy, covering data acquisition, storage, versioning, documentation and access on enterprise infrastructure.

You will design, build, and operate pipelines for structured and unstructured data, including official statistics, licensed data, high-frequency indicators, web data and information extracted from documents. You will turn these into trusted, reusable data products with appropriate metadata, lineage, vintage tracking and validation.

A key part of the role is understanding the specific requirements of economic data, including revisions, rebasing, seasonal adjustment, frequencies, units and data vintages. You are not expected to construct econometric models but should understand their data needs and limitations.

You will also support AI use cases by developing validation frameworks for LLM-based extraction and classification, and by making data assets safely accessible to AI-assisted workflows within MAS's governance framework.

[What you will be working on]

Data Strategy and Stewardship

• Own EPG's data inventory and standards for acquisition, storage, versioning, documentation and sharing.

• Partner with economists to translate policy and research needs into prioritised data requirements.

• Work with data owners, platform, governance and security teams to bring data assets onto enterprise infrastructure with appropriate controls.

Data Engineering and Products

• Design and operate production-grade pipelines across statistical, licensed, API, database, file and web sources, with robust validation, monitoring and recovery.

• Build research-grade datasets that handle revisions, vintages, seasonal adjustment, frequency conversion, rebasing and other economic data requirements.

• Develop reusable data products and real-time/high-frequency pipelines, with clear schemas, lineage, versioning and programmatic access.

• Connect data pipelines and products to downstream analytical programmes and BI tools.

AI Enablement and Engineering Excellence

• Build and validate pipelines that extract structured information and insights from documents and text using text analytics and large language models.

• Make data assets discoverable and safely consumable by AI-assisted workflows through governed, reusable access patterns.

• Apply and promote strong engineering practices, documentation and monitoring, while guiding junior engineers and representing EconTech in technical discussions.

[What we are looking for]

Qualifications and Experience

• Bachelor's degree or higher in a relevant quantitative discipline postgraduate qualifications in Economics or Statistics are an advantage.

• Around 5-8 years of experience in data engineering, data platforms or statistical systems, supporting economists or quantitative researchers.

• Experience in central banking, economic policy, official statistics, international organisations or financial-sector research is an advantage.

Economic Data Literacy

This is a core requirement of the role. Candidates should bring or be able to quickly acquire:

• Familiarity with key macroeconomic and financial datasets, including how they are sourced, published, revised and maintained across statistical agencies and commercial providers.

• Working knowledge of economic time-series issues such as vintages, seasonal adjustment, frequency conversion, rebasing, nominal versus real values, and classification changes.

• Understanding of how data is used in economics research and policy analysis, including the requirements of empirical analysis, nowcasting and forecasting.

Technical Competencies

• Strong proficiency in Python/R and SQL for data transformation, validation and automation.

• Hands-on experience building and operating cloud-based ETL/ELT pipelines, including orchestration and distributed processing frameworks.

• Experience integrating structured and unstructured data from APIs, files, databases, feeds and web sources, with practical data modelling for time-series and panel data.

• Working knowledge of LLM-based information extraction, data governance and sound engineering practices such as Git, testing, CI/CD, logging and monitoring.

Core and Behavioural Competencies

• Strong communication skills, with the ability to bridge economists and data engineers.

• Highly hands-on, with a willingness to build, troubleshoot and operate solutions directly.

• Ability to deliver end-to-end data solutions independently while collaborating across teams.

• Sound technical judgement and curiosity about economics and policy.

Interested candidates should submit their curriculum vitae highlighting relevant data engineering and data product experience, with emphasis on work done for economists, researchers or statistical teams. Candidates are encouraged to include a brief description (one to two pages) of a data pipeline or dataset they have designed and built for economic, financial or statistical analysis, explaining the data sources involved, the quality and validation issues they had to handle, and how the data was used.

You will be working in a fast-paced environment that would require the ability to manage multiple priorities and needs of stakeholders, as well as the agility to respond to changes and developments.

This contract ends in Dec-2028. As part of the shortlisting process for this role, you may be required to complete a medical declaration and/or undergo further assessment.

All applicants will be notified on whether they are shortlisted or not within 4 weeks of the closing date of this job posting.