Data Engineer (ACRA)
- Compensation
- Not disclosed
- Location
- Singapore, Singapore
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Contract
- Experience
- 2+ years
- Category
- Data engineering
- Posted
Skills
Must have
- Bachelor's degree in Computer Science, Information Technology, Data Engineering, or a related field
- 5–8 years of experience in data architecture and engineering, with a track record of large-scale project delivery, preferably in the Singapore Government context
- 3–5 years of experience with cloud technologies including AWS infrastructure and native tools such as CloudWatch and Glue
- 3–5 years of experience in data modelling, data access, and storage infrastructure including Data Lakes, Data Virtualisation, and Data Warehouses
- 2–4 years of hands-on production experience with a major data platform such as Databricks, including experience with Lakehouse architecture, batch and streaming pipelines, and SQL-based analytics; exp
- Experience with data governance capabilities such as Unity Catalog, access control, lineage, workload orchestration, and performance optimisation; familiarity with Databricks governance tooling is pre
- Relevant data engineering certification is preferred; Databricks Certified Data Engineer Associate or Professional certification is a plus
- 2–4 years of experience working with REST APIs and web request protocols
- Experience implementing DataOps practices and advanced data integration techniques such as data streaming, CDC, and message queues
- Strong analytical thinking with the ability to break down complex problems and coordinate delivery across teams
- Strong leadership and communication skills, with experience mentoring junior engineers
Full description
The Government Technology Agency (GovTech) is the lead agency driving Singapore’s Smart Nation initiatives and public sector digital transformation. As the Centre of Excellence for Infocomm Technology and Smart Systems (ICT & SS), GovTech develops the Singapore Government’s capabilities in Data Science & Artificial Intelligence, Application Development, Smart City Technology, Digital Infrastructure, and Cybersecurity.
At GovTech, we offer you a purposeful career to make lives better. We empower our people to master their craft through continuous and robust learning and development opportunities all year round. Our GovTechies embody our Agile, Bold and Collaborative values to deliver impactful solutions.
GovTech aims to transform the delivery of Government digital services by taking an "outside-in" view, putting citizens and businesses at the heart of everything we do. Play a part in Singapore’s vision to build a Smart Nation and embark on your meaningful journey to build tech for public good.
Join us to advance our mission and shape your future with us today!
Learn more about GovTech at tech.gov.sg
(What you will be working on)
You will be embedded within the Accounting and Corporate Regulatory Authority's (ACRA) team, contributing to the Enterprise Data Hub (EDH) — a key whole-of-government data infrastructure initiative. The EDH is a centralised data sharing platform purpose-built to enable agencies across the Singapore Government to access, share, and derive meaningful value from data in a governed, scalable, and secure way. The work is technically challenging and consequential — you will be architecting and engineering data infrastructure that shapes how the Singapore Government shares and uses data for years to come.
In this role, you will own complex assignments spanning the full data lifecycle, from architecture and pipeline development to governance and quality assurance, while mentoring junior engineers and contributing to cross-team initiatives.
Data Infrastructure and Pipeline Development
Design and build moderately complex data systems, including data models, integration frameworks, and storage solutions. Architect robust pipelines using advanced ETL/ELT techniques, data streaming, CDC, and message queues. Implement scalable solutions using tools such as Databricks, including Lakehouse architecture, Delta Lake storage patterns, and production-grade batch and streaming pipelines. Govern schema design standards and data integrations across multiple source systems.
Performance Optimisation and DataOps
Implement DataOps practices including automated deployments, pipeline monitoring, and data observability. Optimise system performance to handle growing data volumes, proactively address technical debt, and apply tools such as Databricks to improve governance and operational efficiency.