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ETL QA Engineer

Los Angeles, California, United StatesOnsiteFull TimePosted 2 months agoVisa sponsorship available

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Role summary

Seeking an ETL QA Engineer to validate data accuracy and integrity within Property & Casualty Insurance projects. Responsibilities include performing data reconciliation, verifying ETL transformations, and conducting end-to-end data validation. The role requires advanced SQL skills, hands-on ETL testing experience, and knowledge of SDLC/STLC in data-centric environments. Experience with Agile/Scrum methodologies and tools like Rally is essential. The engineer will create test strategies, execute various test types, log defects, and generate data quality reports to ensure compliance with domain rules.

Job Description
< class="text-capitalize mb-0 lh-base fw-600 pt-0 mb-1 text-break fs-24">Job Title: ETL QA Engineer
Location: Woodland Hills, CA/Los Angeles, CA
Required Skills

  • Agile/Scrum experience
  • Strong analytical and decision-making skills
  • Good communication and stakeholder management
  • Domain knowledge: Property & Casualty Insurance (Auto, Home, Umbrella)
  • Experience with Rally (or similar tools)

Roles and Responsibilities

  • Validate source-to-target mappings and business rules
  • Perform data reconciliation between source and target systems
  • Verify ETL transformations (aggregations, calculations, derivations)
  • Conduct end-to-end data validation (Source → Staging → Target)
  • Ensure compliance with P&C domain rules
  • Collaborate with business SMEs to ensure data accuracy
  • Create and maintain test strategy, test cases, and validation checklists
  • Design and execute tests for full, incremental, delta, and historical loads
  • Perform SIT, UAT support, regression, reconciliation, and smoke testing
  • Log, track, and analyze defects; support resolution with dev teams
  • Validate fixes and generate data quality/test reports

Required Experience

  • Hands-on ETL testing and data validation experience
  • Strong understanding of ETL architecture and data pipelines
  • Advanced SQL skills (joins, aggregations, complex queries)
  • Knowledge of SDLC/STLC in data-centric projects
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