Delivery jobs in Singapore

The Data Engineer designs, builds, and maintains the organisation’s enterprise data architecture.

This role ensures that data across HQ and Research Institutes is reliable, secure, and analytics ready. The Data Engineer also drives standards, performance optimisation, and automation for scalable analytics and AI delivery.

• Design and implement robust data pipelines using Azure Data Factory (ADF) and ELT/ETL best practices.
• Build and optimise data marts to support enterprise dashboards and analytics tools (e.g., Power BI).
• Optimise Snowflake environments for performance, cost, and scalability.
• Partner with IT Shared Services to ensure compliance and enterprise security standards.

• Administer Snowflake accounts, roles, warehouses, and data‑sharing configurations.
• Integrate metadata and lineage flows into Alation to enhance discoverability and governance.
• Implement automation pipelines and CI/CD practices for ingestion, validation, and deployment.

• Develop and maintain validation checks, profiling, and error‑handling logic within pipelines.
• Collaborate with Data Analysts and Scientists to ensure datasets meet analytical and AI model requirements.

• Partner with Business Analysts and the AI team to operationalise dashboards and machine‑learning outputs.
• Mentor junior engineers in coding, documentation, and data‑architecture best practices.

• Bachelor’s degree in Computer Science, Data Engineering, Information Technology, or a related discipline.
• At least 8–10 years of experience in data engineering or platform development roles.
• Hands‑on experience with Snowflake, Azure Data Factory, and metadata catalogues (e.g., Alation, Collibra).
• Proficient in SQL, Python, and data‑orchestration tools (e.g., Airflow, dbt).
• Strong knowledge of data modelling, cloud‑warehouse architecture, and CI/CD automation.
• Familiar with public‑sector data governance and compliance.
• Demonstrated ability to collaborate across analytics, AI, and IT teams.