Senior Data Engineer

4 days ago

Singapore Opensource Full time $120,000 - $180,000 per year

Role Overview

The Senior Data Engineer is responsible for designing, building, and maintaining large-scale, secure, and high-performance data pipelines supporting critical Financial Services workloads.

The role focuses on data modernization, regulatory data aggregation, and AI/ML enablement across domains such as Core Banking, Payments, Risk, Treasury, and Regulatory Reporting.

  1. Key Responsibilities

  2. Design, implement, and optimize ETL/ELT data pipelines using Apache Spark, PySpark, Databricks, or Azure Synapse.

  3. Build and operationalize real-time streaming pipelines leveraging Kafka / Confluent / Azure Event Hubs for risk and liquidity data.
  4. Integrate and transform data across Core Banking, Trade, Payments, Treasury, CRM, and Compliance systems.
  5. Implement data quality, validation, and lineage controls using tools such as Great Expectations / Deequ / dbt tests.
  6. Develop and maintain data models and schemas (3NF, Dimensional, Data Vault 2.0).
  7. Collaborate with Security and Governance teams to implement data security, masking, encryption, and tokenization in compliance with MAS TRM / PDPA / PCI-DSS.
    Participate in data platform modernization projects (Teradata / DB2- Snowflake / Databricks / Synapse).
  8. Collaborate with Data Scientists and AI Engineers to deploy ML feature stores and model-serving pipelines.
  9. Support regulatory reporting (MAS 610/649) and Basel III/IV data flows.

  10. Maintain CI/CD pipelines for data infrastructure using Azure DevOps / Terraform / GitHub Actions.

  11. Required Technical Skills

  12. Category Tools / Technologies

  13. Languages Python, PySpark, SQL, Scala
  14. Data Platforms Azure Data Lake, Synapse, Databricks, Snowflake
  15. Orchestration Apache Airflow, Azure Data Factory, dbt
  16. Streaming Kafka, Confluent, Event Hubs
  17. Governance Apache Atlas, Azure Purview, Collibra
  18. Security Encryption, RBAC, Tokenization, Audit Logging

  19. CI/CD & IaC Terraform, Azure DevOps, GitHub Actions

  20. Experience and Qualifications

  21. 6 – 10 years of experience in data engineering, with at least 3 years in BFSI (banking, insurance, or capital markets).

  22. Proven experience building real-time and batch data pipelines on Azure or AWS.
  23. Exposure to regulatory data models (MAS 610, Basel III, IFRS 9/17, BCBS 239).
  24. Familiarity with DevOps and MLOps integration.
  25. Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field.

  26. Certifications preferred: Microsoft Azure Data Engineer Associate, Databricks Data Engineer Professional, Snowflake SnowPro Core.

  27. Key Attributes

  28. Strong analytical and problem-solving mindset.

  29. Ability to work across multi-disciplinary and geographically distributed teams.
  30. Excellent written and verbal communication skills.
  31. High accountability and ownership for quality and delivery.

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