About the role
Job Description: Data Test Automation Engineer (Data Platform)
Location: Singapore/Bangalore
About Us
Dymon Asia Capital (“Dymon Asia”), founded in 2008, is a leading alternative investment firm with assets across public and private markets. The firm’s flagship hedge fund product is the Multi-Strategy Investment Fund (MSIF), an Asia-focused multi-manager multi-strategy fund that seeks to generate absolute consistent uncorrelated returns from emerging and developed markets.
Headquartered in Singapore, Dymon Asia operates from nine offices across the Middle East and Asia, including Hong Kong, Tokyo, Dubai, Shanghai, Mumbai, and Kuala Lumpur. Our platform is built on our people. We bring together diverse perspectives, expertise, and experience across our multi-strategy and private markets businesses. Maintaining a collaborative culture where our people are set up to do their best work is central to how we operate.
We are looking for a Senior SDET / QA Automation Engineer to lead quality engineering across our data platform. This role is suited to a hands-on engineer who builds automation rather than executes manual tests, and who wants to ensure the data behind trading, risk and investment decisions is accurate, reliable and performant.
Working closely with Data Architects, Data Engineers and Developers, you will embed automated testing within each sprint and validate large-volume pipelines built with Python, PySpark and modern Lakehouse technologies.
Key Responsibilities
- Design and implement automated functional, regression, integration and performance tests for data platforms and pipelines.
- Develop and maintain test automation using Python and PySpark.
- Validate data ingestion, transformations and processing through data quality, source-to-target reconciliation and business-rule validation.
- Work closely with Data Architects and Developers to define test coverage and identify quality risks early in the development lifecycle.
- Deliver test automation within the sprint, alongside feature development.
- Build reusable, maintainable automation frameworks and integrate automated tests into CI/CD pipelines.
- Test large-volume data processing for performance, scalability and reliability.
- Investigate data discrepancies, defects and performance issues, working with engineering teams to identify root causes.
- Contribute to sprint planning, refinement and technical discussions, promoting testability and quality engineering practices.
Job Requirements
- Degree in Computer Science, Engineering, Mathematics, or a related discipline preferred.
- 6+ years of experience in SDET, QA Automation, Quality Engineering or a similar hands-on technical testing role, with a proven track record of developing automated tests rather than primarily executing manual testing.
- Strong experience testing data platforms, ETL/ELT pipelines, data warehouses/lakes or other data-intensive systems, including data quality, reconciliation and transformation validation.
- Strong programming and test automation skills in Python, with hands-on PySpark experience testing large datasets.
- Strong SQL skills for data validation and analysis.
- Experience building maintainable automated test suites and frameworks and implementing them within CI/CD pipelines; pytest or similar Python testing frameworks preferred.
- Experience across functional, regression, integration and performance testing, including scalability and high-volume data testing, within Agile teams using a shift-left, in-sprint automation approach.
- Strong analytical, debugging and problem-solving skills, with the ability to work independently while collaborating closely with Data Engineers, Developers and Architects.
- Domain exposure highly desirable: hedge funds, asset management, investment banking or capital markets, including familiarity with financial datasets such as trades, positions, market data, P&L and risk data.
- Exposure to Databricks and Lakehouse platforms, Delta Lake and Spark-based processing, Azure, AWS or GCP, data quality/observability or orchestration tools, and data governance, lineage and metadata concepts is a plus.