Senior Data Engineer
Summary
Senior Data Engineer on the DSAD Platform and Architecture team, designing, building, productionising and operating trusted data pipelines and reusable data products on AWS and Databricks. Day-to-day work spans PySpark/SQL transformations, lakehouse architecture, ingestion/CDC patterns, data quality and Terraform CI/CD. Onsite at Paya Lebar, Singapore; typically 6–10 years' experience.
Senior Data Engineer
Work location- Paya Lebar
The Senior Data Engineer sits within the DSAD Platform and Architecture Team and is responsible for designing,
building, productionising and operating trusted data pipelines and reusable data products on the data platform using AWS and Databricks.
Experience: Typically 6–10 years of relevant data or software engineering experience, including hands-on design and
operation of production data pipelines and at least 2 years working with cloud-based data platforms.
- Strong Python, PySpark and SQL capability, including distributed processing, complex transformations, query optimisation, debugging and automated testing.
- Hands-on experience with Databricks, Apache Spark and Delta Lake for production-scale batch or streaming data engineering.
- Hands-on AWS data engineering experience with a relevant mix of S3, Glue, Lake Formation, IAM, KMS, Lambda, Step Functions, EventBridge, CloudWatch, Redshift and MSK/Kinesis.
- Experience designing lakehouse or data-lake architectures, including layered data patterns, dimensional or domain data modelling and reusable curated datasets.
- Experience implementing incremental ingestion, change data capture, idempotency, late-arriving data handling, backfills, replay and recovery.
- Experience implementing data quality, schema validation, reconciliation, metadata and lineage as automated parts of production pipelines.
- Experience with workflow orchestration, Git-based development, CI/CD, infrastructure as code such as Terraform, and environment promotion controls.