Senior Data Engineer (GeBIZ X)

The Ministry of Finance is seeking a dynamic and strategic Data Engineer to design, build, and scale enterprise-grade data platforms and pipelines for the GeBIZ X product.

You will play a key role in shaping the product's data strategy, ensuring high standards in data architecture, performance, scalability, and governance. Beyond core data engineering, you will drive innovation in AI-enabled capabilities, leveraging data as a foundation to unlock insights, improve decision-making, and enhance user outcomes across the public service.

Responsibilities:

• Data Strategy, Engineering & Product Delivery:
• Engage stakeholders to understand business challenges, refine use cases, and identify opportunities to harness data for product and policy decisions.
• Design, build, and maintain scalable data pipelines, data platforms, and reusable datasets to support analytics and product use cases.
• Develop data models and architectures to support complex business processes and reporting needs.
• Implement ETL/ELT processes to ingest, transform, and serve structured and unstructured data.
• Design and deliver analytics workflows, dashboards, and data products to enable insights and decision-making.
• Work iteratively with product teams to validate insights, refine analyses, and deliver measurable outcomes.

• AI, Machine Learning & Advanced Analytics:
• Develop and apply machine learning and Large Language Model (LLM)-based solutions, including prompt engineering and AI-enabled applications where relevant.
• Operationalise models through MLOps practices, including deployment, monitoring, and lifecycle management.
• Stay abreast of emerging trends in AI, data architectures, and optimisation techniques, and apply them to enhance product capabilities.
• Ensure responsible AI practices, including considerations for safety, fairness, and robustness.

• Data Platforms, Integration & DataOps:
• Design and implement moderately complex data systems and platforms, including supporting infrastructure and integrations.
• Apply advanced data integration techniques (e.g. streaming, Change Data Capture, message queues) to enable real-time and batch processing.
• Champion DataOps practices such as automated pipeline deployments, monitoring, and data observability.
• Ensure data quality, reliability, and integrity across systems and pipelines.
• Proactively manage and reduce technical debt to ensure long-term system stability.

• Governance, Risk & Compliance:
• Navigate data privacy, governance, and regulatory requirements, ensuring compliance with policies and standards.
• Identify risks in data systems and proactively address potential failure points.
• Align system design with security, performance, and compliance best practices across GovTech platforms.

• Leadership, Collaboration & Delivery:
• Own and deliver complex data engineering assignments across the full data lifecycle with minimal supervision.
• Break down complex problems into actionable tasks and coordinate delivery across teams.
• Drive high-quality, scalable delivery through engineering excellence and best practices.
• Mentor junior engineers and guide peers through code reviews, architecture discussions, and technical problem-solving.
• Collaborate effectively with Product Managers, Engineers, Designers, and Business stakeholders to deliver integrated solutions.

• Strategic Alignment & Organisational Impact:
• Contribute to team strategy, balancing short-term delivery with long-term platform sustainability.
• Evaluate new technologies and communicate trade-offs to technical and non-technical stakeholders.
• Participate in cross-team initiatives and align solutions with broader organisational goals.
• Contribute to GovTech communities, shaping best practices and promoting a culture of continuous learning and improvement.

Requirements:

• Experience & Education:
• Bachelor's degree or higher in Data Science, Computer Science, Statistics, Applied Mathematics, or a related quantitative discipline.
• At least 5 years of experience in data engineering.
• Proven experience delivering data platforms or products in large-scale, enterprise environments.

• Technical Skills:
• Strong expertise in data modelling (OLTP, OLAP, dimensional modelling).
• Experience with cloud data platforms and tools (e.g. AWS, Azure Synapse/Microsoft Fabric, Snowflake, Databricks, Redshift, Data Lakes).
• Proficiency in big data technologies (e.g. Hadoop, Spark, Kafka, Flink).
• Strong programming skills in Python, Scala, or Java.
• Experience with CI/CD and DataOps practices (e.g. SHIP-HATS or equivalent).
• Expertise in data integration techniques (e.g. ETL/ELT, streaming, APIs).

• AI & Machine Learning (Advantageous):
• Understanding of machine learning and LLM concepts, including prompt engineering and AI application development.
• Familiarity with MLOps practices and cloud-based ML deployment.
• Awareness of responsible AI principles (fairness, robustness, safety).

• Core Competencies:
• Strong analytical and problem-solving skills, with the ability to translate business problems into data solutions.
• Ability to design scalable, reliable, and maintainable data systems.
• Effective communication and stakeholder management skills.
• Self-driven, adaptable, and able to operate in ambiguity.
• Strong sense of ownership and commitment to delivery excellence.
• Passion for leveraging data and technology to deliver public good.