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.

Work arrangement
No