Job Summary

We're looking for a Data QA Engineer to join our data engineering team and own quality across our data platform. You'll work closely with backend, analytics, and product teams to ensure the accuracy, completeness, and reliability of data flowing through our OLTP and OLAP systems.

This is a hands-on role for someone who thinks in data lineages, writes SQL fluently, and cares deeply about building automated test coverage that scales with a complex, microservice-driven product.

Responsibilities for a QA Engineer

• Design, implement, and maintain automated test suites for data pipelines and transformations (Python + testing frameworks)
• Write and execute complex SQL queries to validate data integrity across RDS and OLAP systems
• Trace data lineages to identify root causes of data quality issues across multiple services and teams
• Perform API and backend testing to verify data correctness at ingestion and transformation layers
• Collaborate with engineers across teams to define testability requirements and data contracts
• Maintain test cases and documentation in a TMS
• Participate in incident response and post-mortem analysis related to data quality
• Contribute to testing methodology and best practices within the data engineering function

Qualifications for a QA Engineer

• 3+ years of commercial experience in a data QA or similar role
• Strong hands-on experience with Linux and SQL on RDS databases
• Deep practical knowledge of both OLAP and OLTP systems and their differences
• Proven ability to write and execute complex SQL queries for data validation
• Experience tracing data lineages in multi-system environments
• Proficiency in Python and data testing frameworks for test automation
• Experience with API and backend testing
• Background working on complex software products with multiple teams, cross-service dependencies, and intricate business logic
• Good understanding of testing methodologies (test planning, coverage, regression, exploratory)
• Familiarity with microservice architecture principles
• Experience with a TMS tool such as Zephyr, TestRail, or equivalent
• English: Intermediate or higher (written and spoken)

Nice to have

• Experience with data warehousing tools (e.g., Snowflake, BigQuery, Redshift, dbt)
• Familiarity with data observability or monitoring platforms (e.g., Monte Carlo, Great Expectations, dbt tests)
• Exposure to streaming data platforms such as Kafka or Kinesis
• Understanding of CI/CD pipelines and integrating data tests into deployment workflows
• Experience with cloud infrastructure (AWS, GCP, or Azure) in the context of data engineering
• Knowledge of data governance, cataloguing, or MDM concepts