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.

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.

KPI & Data Validation

• 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.

ETL / ELT & Data Pipeline Validation

• 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.

Test Automation & Quality Engineering

• 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.

AI-Assisted Quality Engineering

• 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.