Onit

AI-driven enterprise legal management platform

Lead Quality Assurance Engineer

Test Automation EngineeringConfirmed live in the last 24 hours

Full-timePune, Maharashtra, IndiaNo salary listedIn-person

About the Job

Requirements

Strong expertise in data quality, analytics/report testing, ETL validation, SQL, and test automation.
Hands-on experience testing Tableau and Superset dashboards and reports, validating data across data-pipeline layers, and independently verifying business KPIs and metrics by writing SQL queries.
Strong understanding of the data lifecycle from source systems through ETL/ELT pipelines and data warehouses to dashboards and reports.
Ability to identify data discrepancies, perform reconciliation, validate transformation logic, and conduct root-cause analysis when reported metrics do not match underlying data.
Practical experience using AI engineering tools such as Cursor, Claude, or equivalent tools for test development, SQL generation, automation, troubleshooting, test-case creation, and productivity improvement.

Responsibilities

Perform hands-on testing of Tableau and Superset dashboards, reports, and analytics capabilities.
Validate dashboard data against underlying databases, data warehouses, and source systems.
Validate dashboard filters, parameters, calculated fields, dimensions, measures, aggregations, and drill-down functionality.
Verify reports accurately represent underlying business data and reporting requirements.
Validate dashboards across different datasets and business scenarios.
Identify and troubleshoot discrepancies between reports and underlying data.
Understand business KPIs, metrics, calculations, and reporting definitions.
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.
Reconcile data between source systems, data warehouses, and reporting layers.
Validate data accuracy, completeness, consistency, uniqueness, and integrity.
Work with Product and Analytics teams to make KPI definitions and acceptance criteria clear, measurable, and testable.
Perform end-to-end ETL/ELT testing and data validation across source, staging, transformation, warehouse, and 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.
Reconcile large datasets across different stages of the data pipeline.
Troubleshoot data discrepancies and identify where data quality issues are introduced.
Validate data-pipeline failure, retry, and recovery scenarios.
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 and data-validation scenarios.
Build reusable automation frameworks and quality-validation utilities.
Integrate automated tests and quality checks into CI/CD pipelines.
Improve regression coverage and reduce dependency on manual testing.
Perform root-cause analysis for production and customer-reported issues.
Promote quality engineering and defect prevention throughout the development lifecycle.
Use AI-assisted engineering tools in day-to-day work for 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.
Evaluate new AI tools and techniques that can improve quality, test coverage, and engineering productivity.
Provide guidance and mentorship to other Quality Engineers while remaining actively hands-on with SQL, data validation, automation, troubleshooting, and testing.

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.

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.

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.

Benefits & Perks That Support You:

Onit offers a comprehensive total rewards package designed to support the whole employee at work and beyond:

Health Coverage: Employee and immediate family members.

Time Away: Flexible paid time off and 10 company paid holidays annually.

Family Support: Exceptional paid leave for birth parents, non-birth parents, and caregivers. Onit also offers surrogacy and adoption reimbursement.

Income Protection: 100% employer-paid life and disability insurance.

Additional Coverage Options: Voluntary benefits including hospital indemnity, critical illness, accident.

Tax-Advantaged Accounts: Flexi, NPS.

Community Engagement: One paid volunteer day each year to give back to the community.

Our Commitment to Applicants

We know that not everyone will check every box in a job description. At Onit, we value diversity, inclusion, and authenticity. If you’re excited about this role but your experience doesn’t align perfectly with every qualification, we encourage you to apply. You may be exactly who we’re looking for.

Onit Values

Customer First - Customer success is our success. We deliver value, listen, and act on customer needs.

Purposeful Innovation - Innovation fuels our growth. We harness creativity to solve problems and lead with the intentions and expertise.

Win as One - Teamwork is how we win. We are accountable, act with integrity, and communicate openly.

Intentional Growth - Our people are the difference. We create an environment with compelling work, impactful contributions, and career growth.

About the Company

Onit

AI-driven enterprise legal management platform

Onit provides AI-native enterprise legal management and legal operations software for corporate legal departments, legal teams, and law firms. Its platform unites core legal ops capabilities—legal spend management, matter management, e-billing, contract lifecycle management, intake and workflow automation—and adds AI-driven review of invoices, contracts, and matters into a single connected system. The product is delivered as a SaaS subscription and now includes AI agents and generative AI across spend, contract, and workflow tools. What sets Onit apart is a platform built specifically for legal needs, designed to scale for regulated industries, and integrated with AI across multiple modules to streamline operations in one environment. The goal is to help enterprise legal teams run their operations more efficiently, control costs, shorten contract cycles, and improve compliance and governance.

Company size: 501-1,000
Company stage: Growth Equity (Venture Capital)
Total funding: $226.4M
Headquarters: Atlanta, GA
Founded: 2009

Recently Posted Jobs

Explore more opportunities at Onit

See more jobs