Quality Analyst

The people here at Apple don't just create products, they create the kind of wonder that's revolutionized entire industries. It's the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Our team produces the foundation, tools, and support that enable Corporate FP&A and Investor Relations to provide the financial insight that our executives need to keep Apple as the preeminent company in the world.

As a Quality Analyst on the Corporate FP&A Data Analytics team, youll 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, youll also judge how well the AI holds up in production, looking past a simple pass/fail to whether the outputs can be trusted. Youll bring a solid foundation in quality assurance, data analysis, and system testing, and apply that same rigor whether youre validating a data pipeline or a models output. Learn how Apples complex financial and hierarchical data behaves across hierarchies and source systems, and let that shape the tests you design. Execute the test strategy against the Product Managers functional requirements, covering new development, regular data loads, and regression. Drive test coverage across source systems, transactional applications, and reporting environments, with repeatable scenarios that make data-quality monitoring routine. Validate how AI and LLM-based features behave in production, judging outputs for accuracy, grounding, and consistency rather than a simple pass/fail. Evaluate release readiness on data quality and functional behavior, and give the go/no-go recommendation during release coordination. Act as the first line for data-quality issues: confirm the behavior, set priority with the Product Manager, and track each one through to a workaround or fix. Work with engineers to understand the implementation logic behind what youre testing. Report quality findings and release-readiness status to the project team and engineering, clearly and on time. 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.