Senior QA Data Engineer (Hybrid, India)

Job summary

This role involves a skilled QA professional focused on validating data platforms, ETL pipelines, analytical models, and large-scale data transformations. The QA Data Engineer collaborates with developers, data engineers, product teams, and business subject matter experts to ensure the quality and accuracy of data assets throughout the delivery lifecycle.

Qualifications

• Proven experience in testing data and analytics platforms.
• Advanced skills in SQL and PL/SQL.
• Hands-on experience with Databricks, including notebooks, Delta Lake, and Spark.
• Strong understanding of ETL concepts and data transformation methodologies.
• Experience with validating large datasets, data pipelines, and data models.

Responsibilities

• Design, develop, and execute test plans for data and analytics products, including ingestion pipelines, transformations, data models, and dashboards.
• Apply testing techniques for data completeness, consistency, accuracy, timeliness, and lineage.
• Write and execute SQL/PL-SQL queries for data verification, profiling, and root-cause analysis.
• Validate end-to-end ETL/ELT workflows and ensure the correctness of data transformations.
• Conduct regression testing following schema evolution, code updates, or pipeline refactoring.
• Collaborate with Data Engineers, Developers-in-Test, Business SMEs, and Product Owners to ensure high-quality delivery.
• Participate in refinement sessions to assess testability of new features and data logic.
• Contribute to automation frameworks for continuous integration of data tests.

Skills

• Desirable skills include Python for test automation and data manipulation, PySpark for distributed data testing, and Pandas for lightweight automation and exploratory validation.
• Exposure to CI/CD, data observability tools, or version control (Git) is a plus.

Education

• Relevant educational background in computer science, engineering, or a related field is preferred.

Tools

• Familiarity with Databricks, SQL/PL-SQL, Python, PySpark, and data observability tools.