Data Quality Engineer

About the Role

We are looking for a hands-on Data Quality Engineer / Quality Engineering Lead to join our Data, Analytics & AI organization in Pune.

The role will be responsible for ensuring that data, analytics, reporting, and AI-enabled products are accurate, reliable, scalable, secure, and production-ready. You will work closely with business, product, data engineering, analytics, technology, and Data Platform teams in India and the U.S.

You will own the end-to-end quality engineering lifecycle — from requirements and test strategy through test automation, data validation, defect management, release certification, and production validation.

This is a highly technical role suited to someone who is comfortable working with large datasets, complex enterprise systems, SQL, data pipelines, Power BI, APIs, cloud platforms, automation frameworks, GitHub, and CI/CD environments.

Key Responsibilities :

1. Quality Engineering & Test Strategy

• Partner with Product, Engineering, Analytics, IT, and Business teams to define quality standards, acceptance criteria, and release-readiness requirements.
• Develop and maintain comprehensive testing strategies and quality frameworks for Data, Analytics & AI products.
• Translate business requirements into structured testing approaches covering functionality, reliability, performance, data quality, and usability.
• Own independent regression testing and release certification.
• Lead defect triage, root-cause analysis, prioritization, remediation tracking, and release sign-off.
• Establish risk-based testing approaches and ensure appropriate quality controls are applied throughout the development lifecycle.

2. Data, Analytics & BI Testing

• Independently test and validate KPIs, business metrics, data transformations, semantic models, dashboards, reports, and analytical products.
• Validate data accuracy, completeness, consistency, reconciliation, and integrity across source systems, integrations, data warehouses, data lakes, and reporting environments.
• Perform advanced SQL-based data validation using complex joins, subqueries, CTEs, window functions, and other data-quality techniques.
• Test enterprise reporting and analytics platforms, including Power BI and governed semantic models.
• Identify data-quality issues and work with Data Engineering and Data Platform teams to perform root-cause analysis and drive remediation.
• Validate that analytical solutions perform reliably and consistently in production environments.

3. Test Automation & Quality Engineering

• Design, build, and maintain automated testing frameworks for Data, Analytics, and AI products.
• Develop automation using technologies such as Selenium, Playwright, Cypress, PyTest, TestNG, JUnit, or equivalent frameworks.
• Integrate automated testing into CI/CD pipelines to enable continuous testing and faster, more reliable releases.
• Drive automation-first and shift-left quality engineering practices.
• Establish testing metrics, quality KPIs, defect trends, automation coverage, and other measures of testing effectiveness.
• Continuously improve testing processes, frameworks, tools, and engineering practices.

4. GitHub, DevOps & Release Management

• Establish and maintain GitHub repository standards for Data, Analytics, and AI solutions.
• Support repository management, branching strategies, pull requests, merge validation, release tagging, and source-control governance.
• Integrate automated quality checks and testing into GitHub Actions and CI/CD pipelines.
• Partner with engineering and delivery teams to ensure appropriate quality gates are incorporated into release processes.
• Support release validation, deployment readiness, and production quality controls.

5. AI & Advanced Analytics Validation

• Independently test AI-enabled applications, recommendation engines, predictive solutions, and Generative AI use cases from a quality, reliability, integration, and production-readiness perspective.
• Validate data flows, application behavior, integrations, performance, and operational reliability of AI-enabled solutions.
• Partner with analytics and data science specialists who own model accuracy, statistical validity, and business/model outcomes.
• Ensure AI-enabled solutions meet defined functional, technical, and operational quality standards before production release.

6. Lab-to-Factory & Production Readiness

• Act as the quality owner for solutions transitioning from Innovation Lab/PoC environments into production or Factory environments.
• Define and manage Factory Acceptance Criteria covering functionality, data quality, performance, governance, supportability, monitoring, and operational readiness.
• Review test evidence, deployment plans, support documentation, monitoring processes, and quality metrics before production approval.
• Provide independent quality sign-off for production readiness.
• Partner with Factory and production teams to ensure solutions can be supported, monitored, maintained, and scaled effectively.

7. Cross-Functional Collaboration

• Work closely with U.S.-based business, technology, product, analytics, engineering, and Data Platform stakeholders.
• Communicate testing outcomes, quality risks, defects, dependencies, and release-readiness clearly to technical and non-technical audiences.
• Build strong working relationships across geographically distributed teams.
• Support data-driven and risk-based decision-making through transparent quality reporting and objective testing outcomes.

Required Qualifications & Experience

• Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, Analytics, or a related technical field, or equivalent practical experience.
• 8–12 years of overall experience, with at least 6 years in Quality Engineering, QA, Data Testing, Test Automation, or related roles.
• Strong experience as a Senior Individual Contributor, Lead, or Quality Engineering Lead owning testing strategy and release quality.
• Experience testing enterprise data, analytics, BI, reporting, data engineering, ML, or AI solutions.
• Advanced SQL skills, including:
• Complex joins
• Subqueries
• CTEs
• Window functions
• Data reconciliation and validation
• Hands-on experience testing Power BI, dashboards, semantic models, governed datasets, and enterprise reporting solutions.
• Experience testing:
• Data warehouses
• Data lakes
• ETL/ELT pipelines
• APIs
• Data integrations
• Business rules
• Strong experience building and maintaining automated testing frameworks.
• Experience with one or more automation technologies such as Selenium, Playwright, Cypress, PyTest, TestNG, or JUnit.
• Experience with Azure DevOps, GitHub, Jira, CI/CD pipelines, and release management.
• Hands-on experience with GitHub Enterprise, including repositories, branching, pull requests, merge validation, release tagging, and source-control governance.
• Strong understanding of Agile methodologies, quality governance, release management, and defect management.
• Experience performing root-cause analysis and partnering with engineering teams to resolve data and application-quality issues.

Preferred Qualifications

• Experience working within an enterprise Data, Analytics & AI organization.
• Experience with one or more of the following:
• Microsoft Fabric
• Databricks
• Snowflake
• Azure Synapse Analytics
• Azure Data Factory
• Azure Data Lake
• Experience implementing DataOps, AnalyticsOps, MLOps, or Quality Engineering practices using GitHub.
• Experience testing AI/ML solutions, recommendation engines, predictive models, or Generative AI applications.
• Experience validating semantic models, business metrics, master data, metadata, and governed analytical assets.
• Experience with Lab-to-Factory, PoC-to-Production, or Product Industrialization models.
• Experience working with enterprise-scale data and analytics platforms.
• Relevant certifications such as ISTQB, Agile Testing, Azure, Databricks, Microsoft Fabric, or Quality Engineering certifications are a plus.