Data Strategy & Program Lead, PART DataOps

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.\\n\\nApple is seeking an experienced Data Strategy & Program Lead to join our Finance Process, Analytics, Reporting & Technology (PART) Data Operations team. You will own the data strategy and roadmap behind the data foundations that power Retail Finance - directing the engineers who build them and partnering closely with our IT and technical partner teams to deliver trustworthy, high-quality data at scale. This is a role for someone who thinks strategically about data, defines what "good" looks like, and orchestrates the people and partners needed to get there. If you're energized by ambiguous problems, cross-functional influence, and turning a portfolio of data initiatives into measurable business impact, we'd love to hear from you.

You will set the direction for how Retail Finance's data is sourced, built, governed, and delivered - translating business needs into a clear technical roadmap and orchestrating the engineers and partner teams who execute it. Today that work spans dozens of data pipelines processing billions of records per day; your job is to ensure that work is prioritized well, built to a high standard, and continuously evolving to meet changing business conditions.\n\nYou will lead through influence as much as authority. You'll direct the priorities and delivery of our data engineering resources and partner with technical teams across Apple, relying on those partners for the deepest engineering expertise. Your value is in connecting the dots: understanding the business deeply, knowing enough about the technology to make sound trade-offs and challenge designs, and keeping a complex set of initiatives moving toward outcomes that leadership can trust.\n\nWe're a growing team with plenty of interesting technical and business challenges to solve. We're looking for a self-starter who learns fast, adapts well to changing requirements, builds trust across organizational boundaries, and is comfortable working with minimal direction in a global, fast-paced, sometimes ambiguous environment.

8+ years in program/project management, data strategy, business analysis, or technical program management roles, with a strong data and technology focus\nDemonstrated experience owning and delivering complex, cross-functional initiatives end-to-end in an ambiguous, fast-paced environment\nStrong technical fluency: able to read and reason about SQL and data pipelines, understand data warehouse/lakehouse architecture, and credibly challenge technical designs and trade-offs\nProven ability to influence and build trust across all levels of an organization, including with senior leadership and technical partner teams\nBS or equivalent degree in Engineering, Computer Science, Finance, Business, or a related field\n

Experience directing, mentoring, or managing engineers, contractors, or technical project teams\nWorking familiarity with the modern data stack and concepts behind technologies like Python, SQL, Airflow, Spark, Trino, Kafka, Docker, Iceberg, and Databricks - enough to set direction and make informed decisions, even if you're not the hands-on builder\nExperience establishing data governance, data quality, and data product frameworks\nProficiency in process mapping, business process analysis, and systems development lifecycles\nFamiliarity with SDLC best practices, version control, and CI/CD\nA point of view on applying AI/LLM tooling to improve analyst and engineering workflows\nExperience with cloud platforms (AWS, Google Cloud Platform, Azure) for data infrastructure\nExperience managing large-scale data migrations or onboarding data from external partners\nFinance and accounting process experience; familiarity with Retail or Sales Finance a plus\nBS or MS in Engineering / Computer Science