Lead Quality Assurance Engineer

Onit; Pune District; Presencial; INR 1.200.000 - 1.800.000; Jornada completa

Descripción de la vacante

Onit is seeking a highly skilled Lead Quality Engineer to own data quality, analytics/testing, and ETL validation across our AI-powered data platform. You will drive SQL-based validation, test automation, and KPI integrity for Tableau and Superset dashboards.

You will mentor a team of quality engineers, collaborate with Product and Analytics, and champion AI-assisted testing methodologies to accelerate delivery and reliability in our data lifecycle from source systems to dashboards.

Formación

• Hands-on Lead Quality Engineer with strong expertise in data quality, analytics/report testing, ETL validation, SQL, and test automation.

Responsabilidades

• Design and implement 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 scenarios.
• Provide leadership and mentorship to Quality Engineers; drive AI-assisted quality engineering practices in the development lifecycle.

Conocimientos

Data quality

Analytics testing

ETL validation

SQL

Test automation

Tableau

Superset

AI tools guidance

Herramientas

Cursor

Claude

Descripción del empleo

About Onit

We're redefining the future of legal operations through the power of AI. Our cutting-edge platform streamlines enterprise legal management, matter management, spend management and contract lifecycle processes, transforming manual workflows into intelligent, automated solutions.

We’re a team of innovators using AI at the core to help legal departments become faster, smarter, and more strategic. As we continue to grow and expand the capabilities of our new AI-centric platform, we’re looking for bold thinkers and builders who are excited to shape the next chapter of legal tech.

If you're energized by meaningful work, love solving complex problems, and want to help modernize how legal teams operate, we’d love to meet you.

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