What are the responsibilities and job description for the Quality Analyst position at Apple?
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, 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.
Minimum Qualifications:
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.
Preferred Qualifications:
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.