{"id":2169459,"url":"https://alion.io/job/r3-consultant-sr-data-qa-engineer","title":"Sr. Data QA Engineer","company":{"id":3800173,"name":"r3 Consultant","domain":"r3consultant.com","url":"https://alion.io/company/r3-consultant","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"QA","role_family":"QA","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":11500,"max_usd":29000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":28},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"Linux","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Scrum","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Unix","optional":false}],"status":"live","first_seen_at":"2026-10-08T18:42:14Z","employer_posted_date":null,"last_verified_at":"2026-10-08T18:42:14Z","board_verified":false,"closed_at":null,"days_open":7,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":7},"description":"Sr. QA Data Engineer\nEmployment Type: Full-Time, Permanent\nLocation: Remote – Pan India\nShift Timings: 11:00 AM – 08:00 PM IST\nReporting To: Sr. Technical Manager / Director of Engineering, as assigned by Management\n\nPosition Overview\n\nWe are looking for an experienced Sr. QA Data Engineer to join our Data Engineering team and take ownership of the quality, accuracy, consistency, and reliability of data pipelines and data-driven systems. \n\nThe ideal candidate will have strong hands-on experience in Data Testing, ETL/ELT Testing, \nDatabase Testing, SQL, Python, Apache Spark, and Databricks. The candidate should be comfortable validating complex data transformations, investigating data discrepancies, and testing end-to-end data workflows across multiple sources, processing layers, and downstream systems.\n\nYou will work closely with Data Engineers, Software Engineers, Database Architects, Data Analysts, Data Scientists, and Product teams to ensure data is processed correctly and delivered reliably to downstream consumers.\n\nThis role requires strong analytical and problem-solving skills, attention to detail, and the ability to independently understand complex data flows, develop comprehensive test strategies, automate data validation, investigate defects, and drive quality across the data engineering ecosystem.\n\nKey Responsibilities\n\nDesign and execute functional, integration, regression, and end-to-end testing for data pipelines and data platforms.\nValidate ETL/ELT workflows, data transformations, business rules, and source-to-target data accuracy.\nTest Apache Spark/PySpark, Databricks, and Delta Lake data processing workflows.\nPerform advanced SQL-based data validation, reconciliation, and data-quality testing.\nValidate data for accuracy, completeness, consistency, uniqueness, integrity, and schema compliance.\nTest APIs and integrations across data sources, databases, pipelines, and downstream systems.\nDevelop and maintain Python/SQL-based test automation for data validation and regression testing.\nDesign and execute positive, negative, edge-case, failure, retry, and recovery scenarios across data workflows.\nInvestigate data discrepancies and work with Data Engineers, Developers, Analysts, and Data Scientists to identify root causes and resolve issues.\nReview requirements, technical designs, and data flows to identify QA risks, dependencies, and coverage gaps early in the development lifecycle.\nMaintain comprehensive test cases, automation suites, defect documentation, test evidence, and release validation for assigned data products.\nDrive continuous improvement of QA processes, data validation frameworks, automation coverage, and overall data quality.\n\nRequired Qualifications & Skills\n\nBachelor's degree in Computer Science, Engineering, Information Technology, or a related field.\n5+ years of relevant QA/testing experience, preferably working with Data Engineering, Big Data, or data-intensive applications.\nStrong hands-on experience in ETL/ELT testing, database testing, and data validation.\nAdvanced SQL skills, including:\nComplex joins\nSubqueries\nCTEs\nWindow functions\nAggregations\nData reconciliation\nPractical experience testing Apache Spark/PySpark data processing workflows.\nHands-on experience with Databricks and Delta Lake.\nStrong understanding of Big Data concepts, distributed data processing, data pipelines, and data architecture.\nExperience validating Parquet, JSON, CSV, and other structured and semi-structured data formats.\nStrong Python programming/scripting skills for test automation and data validation.\nExperience with API and integration testing.\nWorking knowledge of Linux/Unix environments and shell scripting.\nUnderstanding of:\nData Lakes\nLakehouse Architecture\nData Warehousing\nETL/ELT Pipelines\nExperience with Git, defect management tools, and CI/CD-based testing workflows.\nExperience working in an Agile/Scrum environment.\nStrong analytical, troubleshooting, and root-cause analysis skills.\nAbility to work independently, take ownership, and collaborate effectively with cross-functional teams.\nExcellent written and verbal communication skills in English.\n\nCore Technology Areas\n\nData Testing | ETL/ELT | SQL | Python | Apache Spark | PySpark | Databricks | Delta Lake | Database Testing | API Testing | Data Quality | Data Lakes | Lakehouse | Data Warehousing | Linux/Unix | Shell Scripting | Git | 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