QA Engineer

Key Responsibilities: Translate business and technical requirements into detailed, executable test cases. Validate data sources, extraction logic, transformation rules and end-to-end data loading accuracy. Use AI-driven QA tools (JAM, Testsigma, AI accelerators) to improve automation, coverage, and productivity. API Testing: Postman, Swagger, Apache JMeter, SoapUI, and related tools. Design, document, and execute comprehensive test plans, automation suites, and test harnesses. Testing Types: Functional, Non-Functional, Regression, Integration, Performance, Load and Stress testing across distributed systems. Apply QA best practices across SDLC/STLC in Agile, Scrum, Waterfall, and hybrid models. Manage defects with JIRA/Linear, providing clear reporting and root-cause analysis. Use AI to generate, enhance, and maintain automated test scripts and test-data frameworks. Requirements: Strong SQL skills with ability to write and optimize queries for advanced data validation, reconciliation, and diagnostics. Hands-on testing experience across ETL workflows on Snowflake, GBQ, Postgres, Redshift, and other modern data warehouse platforms. Deep experience in Data Warehousing and BI validation with strong understanding of ETL architecture and data quality frameworks. Proficient in validating ETL pipelines, complex transformation logic, job orchestration, and end-to-end data lineage. Strong command of QA methodologies, test design strategies, automation principles, and quality governance models. Ability to create detailed functional, non-functional, and performance-focused test documentation. Strong analytical and communication skills with clear articulation of findings. Proven ability to work independently, unblock issues proactively, and collaborate effectively with minimal supervision.