Quality Analyst

DESCRIPTION

As a Quality Analyst on the Corporate FP&A Data Analytics team, you'll safeguard the quality of the financial data and tools our FP&A and Investor Relations partners and executives depend on. Your day-to-day is rigorous data-quality testing across source systems, data loads, and reporting. As the team ships generative-AI features into those tools, you'll also judge how well the AI holds up in production, looking past a simple pass/fail to whether the outputs can be trusted.

You'll bring a solid foundation in quality assurance, data analysis, and system testing, and apply that same rigor whether you're validating a data pipeline or a model's output.

• 5+ years in quality assurance or data validation, in analytics, BI, or other data-heavy environments.
• BS or MS in Engineering, Computer Science, or a related field.
• Experience applying a data-driven QA approach, including data reconciliation, in business-critical environments.
• Hands-on experience with SQL and at least one analytics or reporting tool (e.g., Business Objects, Tableau), with the ability to independently investigate and validate datasets.
• Experience executing automated test cases with languages or frameworks such as Python, JavaScript, or Selenium.
• Direct experience testing or evaluating LLM-based or AI-powered systems.

• Advanced SQL, including complex joins, aggregations, and large-dataset validation.
• Understanding of dimensional modeling and data warehousing, plus domain experience in BI, financial, or hierarchical data systems.
• Experience validating data pipelines, backend APIs, or service-based systems, including data-migration and source-to-target validation.
• Exposure to automated test-framework design or test infrastructure, and how automated tests fit into CI/CD release workflows.
• Experience designing structured evaluations of model behavior (hallucination detection, grounding checks, consistency analysis), and monitoring how prompt or input changes affect outputs.
• Experience handling confidential, market-sensitive financial data within security and data-governance controls.
• Turns quality findings into clear, actionable updates for technical and business partners; fluent in Jira, Confluence, or Quip.
• Self-directed and detail-oriented, comfortable owning quality without close oversight.