Lead Quality Assurance Engineer

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.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.