Quality Analyst, Data Solutions & Initiatives

Summary

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.

We are looking for a world-class Quality Analyst (QA) to join the Data Solutions & Initiatives (DSI) team. This is a unique opportunity to help the growth of one of Apple’s global initiatives and contribute to launching ground-breaking new features in support of Apple’s demand generation strategy.

For this position, the individual will need a strong foundation in quality assurance, data analysis, and system testing, with the ability to evaluate and support AI-powered and LLM-based features. This role is focused on validating the quality, reliability, and correctness of AI outputs in real production scenarios, not just traditional pass/fail testing.

Are you an individual who possesses the right mix of technical and data analysis skills to successfully assure data quality for strategic initiatives in a fast-paced environment? Come join our team!

Description

Your responsibilities will include:

• Deep dive into various financial and hierarchical data points in different sets of hierarchies, understanding the nuances of how complex data behaves
• Define and implement the test strategy based on functional requirements driven by the product manager
• Work closely with the product managers to define test plans that ensure data quality both on new development and regular data loads
• Drive test coverage across different source systems, transactional applications and reporting environments, taking into account new features and regression
• Execute test scenarios in a repeatable manner, allowing for easy data quality monitoring
• Coordinate release management and hold the final go / no-go in terms of data quality and functional behavior
• Act as first gate for production ops issues, confirm the behavior and makes the call on priority with product manager
• Report findings in a clear, structured, and actionable manner
• Collaborate with engineers to understand implementation logic
• Manage tickets on found data issues and work with the product manager to plan out bug fixes
• Communicating status updates to end users and stakeholders concisely in a timely manner

Minimum Qualifications

At least 5+ years of relevant experience (e.g., QA, Quality Engineering, Data QA, or similar)

Strong communicator with the ability to interpret technical concepts and data findings for non-technical end users

Experience applying a data-driven QA approach, including data reconciliation or validation, in business-critical environments

Experience with automated test framework design or test infrastructure beyond individual test case execution

Hands-on experience with SQL and at least one analytics or reporting tool (e.g., Business Objects, Tableau, or similar), with the ability to independently investigate and validate datasets

Experience executing automated test cases using languages or frameworks such as Python, JavaScript, or Selenium

Experience working in cross-functional teams using Agile frameworks such as Scrum or Kanban

Familiarity with LLM-based or AI-powered systems, including hands-on experimentation or professional exposure

Understanding of prompt-based systems and how changes to prompts or inputs affect outputs

Experience using collaboration and tracking tools such as Jira, Confluence, Quip, or similar

Strong problem-solving skills with attention to detail

Self-motivated, proactive, and able to work independently or as part of a team

Ability to learn quickly and adapt in a fast-paced environment

Bachelor’s degree in Computer Science, Engineering, or Information Systems, or equivalent practical experience

Preferred Qualifications

Experience validating backend APIs, data pipelines, or service-based systems

Strong proficiency in SQL, including complex joins, aggregations, and large dataset validation

Understanding of dimensional modeling and data warehousing concepts

Experience testing data migration projects, including source-to-target validation

Familiarity with CI/CD pipelines and how automated tests fit into release workflows

Exposure to model behavior evaluation, such as hallucination detection, grounding checks, or consistency analysis

Experience working with observability, logging, or monitoring for data or AI systems

Domain experience in BI, financial, or hierarchical data systems

Master’s degree preferred