Cognizant

Discussion

Test Lead

Summary

Leads end-to-end testing for Master Data Management solutions using Reltio MDM, Snowflake, Java, and Python, ensuring high-quality master data across enterprise systems in healthcare.



Job Summary

Experienced MDM Test Lead responsible for planning managing and executing end-to-end testing activities for Master Data Management solutions using Reltio MDM Snowflake Java and Python. Leads functional integration data quality API ETL and regression testing efforts to ensure accurate reliable and high-quality master data across enterprise systems. Collaborates with business users data analysts developers and architects to validate MDM Implementation.


Responsibilities

  • Lead the planning coordination and execution of end-to-end testing activities for Life Sciences Customer MDM programs ensuring high-quality master data management solutions for provider and healthcare organization domains.
  • Define and execute Reltio MDM testing strategies including validation of data models match/merge rules survivorship logic hierarchies workflows data stewardship processes and integration points with enterprise systems.
  • Oversee ETL testing activities by validating source-to-target mappings transformation rules data migration processes reconciliation reports and end-to-end data flows to ensure data accuracy completeness and consistency.
  • Lead Snowflake testing efforts including data validation SQL-based reconciliation reporting verification performance testing and data quality assessments across analytical and operational data environments.
  • Manage test teams review test cases and test results coordinate defect triage and resolution provide status reporting to stakeholders and ensure adherence to QA standards data governance requirements and project delivery timelines.
  • Drive end to end test planning activities by defining scope strategy timelines and resource needs to ensure robust validation of data driven solutions that align with business outcomes and organizational quality standards.
  • Develop detailed test scenarios and test cases for ETL processes data pipelines and applications by translating requirements into clear conditions that fully validate data transformations integrations and system behaviors.
  • Execute functional system integration and regression testing for ETL jobs and related applications using structured approaches that verify data completeness accuracy consistency and performance across all stages.
  • Design and implement automated test scripts using Python to validate data workflows business rules and interfaces thereby improving repeatability reducing manual effort and accelerating release cycles.
  • Create and maintain reusable Python utilities frameworks and data validation tools that streamline test execution increase coverage and enable consistent quality practices across multiple projects and teams.
  • Perform thorough ETL validation by comparing source and target datasets applying complex SQL queries and assuring that transformation logic mappings and aggregations meet specified requirements and regulatory expectations.
  • Use advanced SQL and database skills to profile data identify anomalies troubleshoot defects and confirm that data integrity constraints indexes and performance characteristics support stable and efficient operations.
  • Establish comprehensive test metrics and reporting practices by defining key measures such as defect density execution progress coverage and trends to provide transparent insights for stakeholders and continuous improvement.
  • Prepare clear test status reports dashboards and executive summaries that communicate progress risks defects and readiness in an accessible format enabling informed decisions and timely corrective actions.
  • Collaborate closely with product owners business analysts data engineers and developers in a hybrid work model to clarify requirements resolve issues and ensure that testing feedback is integrated into solution design and delivery.
  • Coordinate defect triage by analyzing impact prioritizing fixes validating resolutions and ensuring that critical issues are addressed before deployment to protect system stability and user confidence.
  • Contribute to test process improvements by identifying gaps introducing best practices in data testing automation and metrics and sharing reusable patterns that enhance quality efficiency and consistency.
  • Apply knowledge of data models and data structures when available to design targeted test cases that validate relationships hierarchies and business rules improving the reliability of analytical insights and reporting outcomes.

    • Qualifications

  • Possess seven to eleven years of professional experience in software testing or quality engineering with a primary focus on data intensive systems ETL testing and enterprise scale validation initiatives.
  • Demonstrate strong hands on expertise in test metrics and reporting including definition of key performance indicators creation of dashboards and interpretation of trends to drive quality decisions and process enhancements.
  • Exhibit solid proficiency in ETL validation activities such as mapping verification data reconciliation source to target comparison and verification of transformation rules across complex data flows.
  • Show advanced capability in Python scripting for building automated tests data validation tools and utilities that interact with databases files and services in order to support scalable reusable test solutions.
  • Display comprehensive skills in database concepts and SQL including joins aggregations window functions and performance tuning techniques for analyzing large datasets and diagnosing data quality issues.
  • Bring exposure to or understanding of data models such as star schemas normalized structures and dimensional modeling techniques to design more effective test coverage for analytical and reporting solutions.
  • Communicate clearly in English with strong reading writing and speaking abilities enabling effective collaboration documentation and stakeholder engagement in a hybrid work environment.
  • Adopt a quality mindset by proactively identifying risks proposing mitigation actions and ensuring that every testing activity contributes to reliable systems that support the company mission and positive societal impact.


    • Certifications Required

    ISTQB Advanced Test Analyst or equivalent and optional certification in data engineering or database technologies such as Microsoft or AWS data specialty.

    Show