[Hiring] Data Quality Management Lead @Zensar

Role Description

We are seeking a hands-on QA Lead to drive quality assurance for a scalable, enterprise-wide data platform for an insurance client. The role involves validating batch and real-time data pipelines, ensuring data accuracy across Raw, Silver, and Gold layers, and supporting report rationalization and self-service analytics (Power BI).

The QA Lead will define and implement end-to-end data testing strategies, covering ingestion (AWS Glue, Kinesis), transformation (DBT), and consumption layers, while ensuring data quality, integrity, and performance optimization.

Key Responsibilities

• QA Strategy & Leadership
  • Define and implement end-to-end QA strategy for the enterprise data platform
  • Establish test frameworks, standards, and governance for data validation
  • Lead QA planning, estimation, and execution across multiple data streams
• Data Validation & Testing
  • Validate data across Raw, Silver, and Gold layers ensuring accuracy, completeness, and consistency
  • Perform source-to-target reconciliation for batch and real-time pipelines
  • Design and execute:
    • Data quality checks
    • Transformation validation (DBT models)
    • Aggregation and KPI validation
• Batch & Real-Time Pipeline Testing
  • Test batch ingestion pipelines using AWS Glue
  • Validate real-time streaming data pipelines using Amazon Kinesis
  • Ensure data latency, sequencing, and event consistency in streaming pipelines
• Reporting & Rationalization QA
  • Validate datasets powering Power BI self-service reports
  • Support report rationalization initiatives by ensuring consistency of KPIs and eliminating redundant data sources
  • Perform report/data reconciliation testing across legacy vs new platform
• Automation & Tools
  • Develop and implement automated data testing frameworks
  • Leverage SQL, Python, and testing tools (e.g., Great Expectations, DBT tests, custom frameworks)
  • Enable continuous testing integration within CI/CD pipelines
• Performance & Optimization Testing
  • Validate performance of:
    • Data pipelines
    • Queries in Snowflake
  • Identify bottlenecks and work with engineering teams to optimize pipelines and queries
  • Ensure scalability for large data volumes and concurrent workloads
• Data Quality & Governance
  • Define and enforce data quality rules, thresholds, and monitoring
  • Implement data anomaly detection and alerting mechanisms
  • Ensure compliance with audit, reconciliation, and governance standards

Qualifications

• Strong experience in data testing / ETL testing / data QA
• Hands-on expertise with:
  • Snowflake (data validation, SQL testing)
  • DBT (testing, model validation)
  • AWS Glue (batch pipeline validation)
  • Amazon Kinesis (real-time pipeline testing)
• Advanced proficiency in SQL for data validation and reconciliation
• Programming skills in Python (preferred)
• Experience in:
  • Data reconciliation (source vs target)
  • Data quality frameworks and validation techniques
  • Automated data testing tools
• Understanding of medallion architecture (Raw, Silver, Gold layers)
• Experience validating Power BI reports and datasets
• Strong understanding of business KPIs and reporting consistency

Requirements

• Experience in Insurance domain (Policy, Claims, Billing data)
• Familiarity with regulatory reporting, audit, and reconciliation requirements
• 8–12 years in QA / Data Testing / ETL Testing
• 3+ years in QA leadership or lead role
• Experience working on enterprise-scale data platforms

Benefits

• We are committed to creating an inclusive workplace.
• All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.