Quality Assurance Engineer - Chennai

Role: Quality Assurance Engineer (Data & API)
SUMMARY The Quality Assurance Engineer partners with Engineering, Data, and Development teams to ensure client requirements are implemented correctly and reliably across highvolume data pipelines. This role reviews requirements, designs test strategies, and executes automated and manual validationemphasizing shiftleft practices and repeatable automation. Candidates are former developers or Software Development Engineer in Test (SDET) who can effectively test large datasets, craft complex SQL for data comparisons, and collaborate within a single, shared sprint cadence. ESSENTIAL DUTIES AND RESPONSIBILITIES Configure, implement, and maintain automated testing frameworks for data and API validation (DBT tests, Pytest, SQL validators). Translate JIRA user stories and acceptance criteria into comprehensive test plans, scenarios, and data validation scripts. Ensure requirements traceability by mapping test cases and validation scripts directly to client requirements and acceptance criteria, maintaining clear documentation throughout the lifecycle. Design and execute unit, integration, smoke, regression, and endtoend tests aligned to the recommended QA & automation framework. Validate large datasets for completeness, accuracy, timeliness, lineage, and schema conformance, author complex SQL for data comparison. Coordinate with Engineering to enable shiftleft testing QA participates in grooming, planning, and daily standups; quality is a shared responsibility. Assist with user acceptance testing (UAT) and production validation, including postrelease smoke testing and regression cycles. Analyze test outputs, identify defects, document issues, and drive rootcause analysis; champion environment parity (VAL mirrors PROD). Contribute to release governance: freeze windows, QA gates, rollback plans, and release logs; ensure test evidence is captured. Continuously improve tools, templates, and processes; propose JIRA automation to autocreate QA stories from engineering stories. Develop domain knowledge of client data products, pipelines, integrations, and KPIs. Maintain comprehensive test documentation, including test cases, scripts, and results, to support audits and compliance. Conduct performance and security testing as required to ensure robust, scalable, and secure solutions. Advocate for user experience and usability in all testing activities. AGILE & COLLABORATION EXPECTATIONS Active participant in Agile ceremonies (grooming, planning, standups, demos, retros) within a single, shared sprint cadence. Story readiness includes defined test criteria and data availability; QA estimates tracked alongside development. Promote predictable velocity by integrating automated tests into CI; definition of done includes tested and validated. Stay current with emerging QA tools, technologies, and best practices; proactively recommend improvements to processes and frameworks. REQUIRED QUALIFICATIONS Former software developer or SDET with handson coding experience (Python preferred). Expertise in SQL and relational databases; able to design complex validation queries for large datasets. Demonstrated experience testing data pipelines, ETL/ELT workflows, and APIs in highvolume environments. Practical knowledge of Airflow (or similar orchestration), DBT, and CI systems; experience validating DAG executions. Proficiency with automated testing tools (Selenium/Cucumber/Playwright for UI where applicable; Pytest for services; DBT for data). Proficiency with version control systems (e.g., Git) and defect tracking tools (e.g., Jira, Azure DevOps). Experience with JIRA and Agile development methodology; comfortable with shared sprint delivery. Strong critical thinking; challenges ambiguous requirements and drives clarity in acceptance criteria. Excellent communication skills; able to partner with Product Owners and Engineers to define testable stories. PREFERRED / DOMAIN EXPERIENCE Experience with liveevent data, broadcast schedules, media assets, or fan engagement platforms. Experience with bigdata validation (Snowflake/BigQuery/Redshift) and performance testing of queries at scale. Familiarity with API contract testing, data lineage tools, and dataset comparison frameworks. ISTQB or equivalent certification is a plus. Experience with cloud-based data platforms (AWS, Azure, GCP) is a plus.