Senior Data Engineer (Paya Lebar)
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Summary
The Senior Data Engineer will design and implement scalable cloud-based data infrastructure, build ingestion pipelines, and enforce data governance policies. The role involves collaborating with stakeholders to deliver data solutions using technologies like Python, SQL, Spark, and various cloud platforms.
Job Scope
- Lead and implement data engineering strategy and architecture blueprints in alignment with business requirements.
- Contribute to evaluation of data platforms and architecture solutions to support evolving data needs. E.g. Data storage / usage for AI purposes.
- Translate business data requirements into technical specifications and scalable solutions.
- Architect and build ingestion pipelines to collect, clean, merge, and harmonize data from diverse sources.
- Design and implement secure, cloud-based data infrastructure and access mechanisms.
- Monitor and optimize ETL systems and databases for performance, reliability, and scalability.
- Construct reusable data models and maintain data catalogues with metadata and lineage using tools such as ER/Studio.
- Collaborate with data stewards to enforce data governance, quality, and security policies.
- Guide agencies through greenfield and brownfield implementations, from problem definition to solution design.
- Develop standardized approaches for assessments, discovery, and solutioning grounded in GovTech best practices.
Champion engineering excellence and influence adoption of modern data and infrastructure practices.
Requirements
- Bachelor's degree in computer science, Software Engineering, Information Technology, or related disciplines.
- 5-10 years of experience in data engineering, cloud infrastructure, or platform engineering
- Deep understanding of data system design, data structures, algorithms, and data architecture modelling.
- Hands-on experience with cloud platforms (AWS, Azure, GCP) and distributed data technologies (Spark, Hadoop).
- Proficiency in Python and SQL.
- Experience with orchestration frameworks (Airflow, Azure Data Factory) and DevOps tools (Docker, Git, Terraform).
- Familiarity with CI/CD pipelines and infrastructure-as-code practices.
- Experience with Databricks / Snowflake / Denodo and implementing batch/real-time data pipelines.
- Strong knowledge of data governance, security, and privacy (especially in public sector contexts).
- Exceptional communication and stakeholder management skills.
- Proven ability to mentor and develop future technical leaders.
Duration/Working Hours
6 months from 1st June 2026, 42 hours / week