{"id":1992734,"url":"https://alion.io/job/onit-lead-quality-assurance-engineer","title":"Lead Quality Assurance Engineer","company":{"id":58716,"name":"Onit","domain":"onit.com","url":"https://alion.io/company/onit","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":null},"role":"QA","role_family":"QA","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":13000,"max_usd":38000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":9},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Cursor","optional":false},{"name":"ETL/ELT","optional":false},{"name":"SQL","optional":false},{"name":"Superset","optional":false},{"name":"Tableau","optional":false}],"status":"live","first_seen_at":"2026-10-07T06:10:47Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-24T16:26:27Z","board_verified":true,"closed_at":null,"days_open":17,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":17},"description":"We are looking for a highly skilled and hands-on Lead Quality Engineer with strong expertise in data quality, analytics/report testing, ETL validation, SQL, and test automation.\nThe ideal candidate will have hands-on experience testing Tableau, Superset dashboards and reports, validating data across different layers of the data pipeline, and independently verifying business KPIs and metrics by writing SQL queries.\nThis role requires a strong understanding of the complete data lifecycle-from source systems through ETL/ELT pipelines and data warehouses to Tableau dashboards and reports. The candidate should be capable of identifying data discrepancies, performing reconciliation, validating transformation logic, and conducting root-cause analysis when reported metrics do not match underlying data.\nThis is a technical leadership role. The Lead Quality Engineer will provide guidance and mentorship to other Quality Engineers while remaining actively hands-on with SQL, data validation, automation, troubleshooting, and testing.\nAI-assisted engineering is a mandatory part of this role. The candidate must have practical experience using AI engineering tools such as Cursor, Claude, or equivalent tools as part of their day-to-day work for test development, SQL generation, automation, troubleshooting, test-case creation, and productivity improvement.\nKey Responsibilities\nAnalytics Testing\nPerform hands-on testing of Tableau Superset dashboards, reports, and analytics capabilities.\nValidate dashboard data against underlying databases, data warehouses, and source systems.\nValidate Tableau Superset filters, parameters, calculated fields, dimensions, measures, aggregations, and drill-down functionality.\nVerify that reports accurately represent underlying business data and reporting requirements.\nValidate dashboards across different datasets and business scenarios.\nIdentify and troubleshoot discrepancies between Tableau Superset reports and underlying data.\nKPI & Data Validation\nUnderstand business KPIs, metrics, calculations, and reporting definitions.\nIndependently translate KPI definitions and business rules into SQL validation queries.\nWrite complex SQL queries to validate metrics displayed in reports and dashboards.\nValidate calculations including counts, sums, averages, percentages, ratios, trends, and period-over-period metrics.\nPerform data reconciliation between source systems, data warehouses, and reporting layers.\nValidate data for accuracy, completeness, consistency, uniqueness, and integrity.\nWork closely with Product and Analytics teams to ensure KPI definitions and acceptance criteria are clear, measurable, and testable.\nETL / ELT & Data Pipeline Validation\nPerform end-to-end ETL/ELT testing and data validation.\nValidate data across source → staging → transformation → warehouse → reporting layers.\nValidate source-to-target mappings and transformation/business rules.\nVerify full loads, incremental loads, historical data loads, and data refresh processes.\nValidate handling of null values, duplicates, missing records, incorrect mappings, and data-type issues.\nPerform reconciliation of large datasets across different stages of the data pipeline.\nTroubleshoot data discrepancies and identify the stage at which data quality issues are introduced.\nValidate data pipeline failure, retry, and recovery scenarios.\nTest Automation & Quality Engineering\nDesign and implement comprehensive test strategies for analytics, reporting, data pipelines, APIs, and application functionality.\nDevelop and maintain automated tests for data validation, API testing, and regression testing.\nAutomate repetitive SQL and data-validation scenarios.\nBuild reusable automation frameworks and quality-validation utilities.\nIntegrate automated tests and quality checks into CI/CD pipelines.\nContinuously improve regression coverage and reduce dependency on manual testing.\nPerform root-cause analysis for production and customer-reported issues.\nDrive a quality engineering and defect-prevention mindset throughout the development lifecycle.\nAI-Assisted Quality Engineering\nUse AI-assisted engineering tools as part of day-to-day work.\nLeverage AI to accelerate:Test-case and test-scenario creation\nAutomation script development\nAPI test creation\nTest-data generation\nLog analysis and troubleshooting\nIdentify opportunities to introduce AI-driven automation into existing Quality Engineering processes.\nContinuously evaluate new AI tools and techniques that can improve quality, test coverage, and engineering productivity.","description_format":"text","description_chars":4548,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Cybersecurity","Document Management","Digital 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