ETL Testing

QA Test Analyst – Commercial Data Products

Duration

Full-time position

Role Purpose

The QA Test Analyst will provide hands‑on testing support across Commercial Data Products, ensuring data accuracy, pipeline stability and high‑quality delivery. The analysts will execute functional, integration, regression and data quality testing, contributing directly to the reduction of defects and operational risk.

Key Responsibilities

• Testing Delivery & Execution
  • Execute Functional, System, Integration and Regression testing.
  • Perform data validation, data quality checks and ETL pipeline testing.
  • Conduct Exploratory testing of new data features and enhancements.
  • Execute structured defect reporting and triage via ADO.
• Data Testing & Analysis
  • Validate transformed datasets, business rules and logic across Claims and Commercial domains.
  • Perform SQL‑based validation, reconciliation and profiling.
  • Support test data creation, preparation and management.
• Automation
  • Execute automated test suites and contribute to automation scripting.
  • Assist in the maintenance of existing automated test packs.
  • Leverage AI‑assisted automation tools (e.g. self‑healing scripts, intelligent locator systems).
• Environment & Tooling Support
  • Support test environment readiness and configuration checks.
  • Validate access, data availability and workflow setup in ADO.

Required Skills & Experience

• Domain Knowledge
  • Commercial, Claims and enterprise‑level data flows.
• Technical & Tooling Experience
  • Azure DevOps (ADO)
  • Data Factory / Databricks
  • Guidewire Claims Centre
  • Sandpiper, ACS, i90, AdGo, Acturis (nice to have)
• Testing Skills
  • Data Quality Testing / Data Validation
  • ETL / pipeline testing
  • Hands‑on SQL expertise
  • Execution of automation scripts beneficial
• Soft Skills
  • Detail‑oriented with strong analytical mindset
  • Ability to follow structured testing processes
  • Effective communicator across Agile delivery teams
  • Proactive in identifying potential risks or data issues