Lead Quality Assurance Engineer
पद का विवरण
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.
The 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.
This 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.
This 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.
AI-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.
Key Responsibilities
Analytics Testing
- Perform hands-on testing of Tableau Superset dashboards, reports, and analytics capabilities.
- Validate dashboard data against underlying databases, data warehouses, and source systems.
- Validate Tableau Superset filters, parameters, calculated fields, dimensions, measures, aggregations, and drill-down functionality.
- Verify that reports accurately represent underlying business data and reporting requirements.
- Validate dashboards across different datasets and business scenarios.
- Identify and troubleshoot discrepancies between Tableau Superset reports and underlying data.
- Understand business KPIs, metrics, calculations, and reporting definitions.
- Independently translate KPI definitions and business rules into SQL validation queries.
- Write complex SQL queries to validate metrics displayed in reports and dashboards.
- Validate calculations including counts, sums, averages, percentages, ratios, trends, and period-over-period metrics.
- Perform data reconciliation between source systems, data warehouses, and reporting layers.
- Validate data for accuracy, completeness, consistency, uniqueness, and integrity.
- Work closely with Product and Analytics teams to ensure KPI definitions and acceptance criteria are clear, measurable, and testable.
- Perform end-to-end ETL/ELT testing and data validation.
- Validate data across source → staging → transformation → warehouse → reporting layers.
- Validate source-to-target mappings and transformation/business rules.
- Verify full loads, incremental loads, historical data loads, and data refresh processes.
- Validate handling of null values, duplicates, missing records, incorrect mappings, and data-type issues.
- Perform reconciliation of large datasets across different stages of the data pipeline.
- Troubleshoot data discrepancies and identify the stage at which data quality issues are introduced.
- Validate data pipeline failure, retry, and recovery scenarios.
- Design and implement comprehensive test strategies for analytics, reporting, data pipelines, APIs, and application functionality.
- Develop and maintain automated tests for data validation, API testing, and regression testing.
- Automate repetitive SQL and data-validation scenarios.
- Build reusable automation frameworks and quality-validation utilities.
- Integrate automated tests and quality checks into CI/CD pipelines.
- Continuously improve regression coverage and reduce dependency on manual testing.
- Perform root-cause analysis for production and customer-reported issues.
- Drive a quality engineering and defect-prevention mindset throughout the development lifecycle.
- Use AI-assisted engineering tools as part of day-to-day work.
- Leverage AI to accelerate:
- Test-case and test-scenario creation
- Automation script development
- API test creation
- Test-data generation
- Log analysis and troubleshooting
- Identify opportunities to introduce AI-driven automation into existing Quality Engineering processes.
- Continuously evaluate new AI tools and techniques that can improve quality, test coverage, and engineering productivity.
KPI & Data Validation
ETL / ELT & Data Pipeline Validation
Test Automation & Quality Engineering
AI-Assisted Quality Engineering
उल्लिखित कौशल

Build better ways to work for your unique business. Our AI-enabled workflow hub connects your teams with how they want to work today and tomorrow. Our purpose is to help connect legal departments more materially to the broader enterprise with customized workflows that meet you where you work. Your Success is Our DNA Built by the pioneers and experts of ELM and CLM, we continually innovate so that we can meet you where you work today and scale to where you’ll be tomorrow. Onit’s comprehensive product portfolio includes enterprise legal management (ELM) and contract lifecycle management (CLM) solutions that automate customized workflows for managing matters, spend, vendors, and contracts. Our mission To transform the legal department beyond business protector and into a business driver that materially influences an enterprise’s growth and efficiency by: Contributing to faster revenue generation Improving operational and cost efficiency Impacting business growth
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