PART DataOps Engineer

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.\\nApple is seeking a senior, hands-on Data Engineer to join the Next-Gen Workflow team within our Finance Process, Analytics, Reporting & Technology (PART) Data Operations group. You'll design and build the data applications - and increasingly, the AI-powered experiences - that change how Apple's Finance analysts work every day. This is a role for an engineer who wants to operate independently end-to-end, help define what AI-native finance tooling looks like at Apple, and see their work used by key stakeholders across the organization.

We are looking for a senior, business- and data-minded engineer with a passion for building intuitive applications that solve real-world business problems. This role sits at the intersection of data engineering, software development, applied AI, and business analysis.\nYou will design, build, and deploy lightweight web interfaces and data tools - primarily using Python frameworks like Streamlit - to streamline financial analysis, reporting, and decision-making. You'll also help lead our team's adoption of AI: both inside our own engineering practice and in the products we ship to analysts, helping them think through where AI can have the biggest impact in their own workflows.\nSuccess in this role requires someone who can quickly absorb complex financial processes, translate ambiguity into a technical roadmap, and deliver high-quality, user-friendly applications with minimal oversight - while raising the bar for the engineers and analysts around them.

6+ years in data engineering or software development, with a strong track record of shipping production data applications end-to-end\nExpert proficiency in Python and hands-on experience building and deploying web applications with Streamlit\nStrong experience with relational databases (e.g., SQL Server, PostgreSQL) and modern data lake / lakehouse environments\nStrong software engineering fundamentals: Git, code review, testing, CI/CD, observability\nBS in Computer Science, Data Science, Engineering, or a related field

Hands-on experience building with modern AI/LLM tooling - e.g., OpenAI / Anthropic APIs, RAG pipelines, agent frameworks, MCP, prompt engineering - and a clear point of view on where AI does and doesn't belong in business workflows\nDemonstrated use of AI-assisted development tools (e.g., Claude Code, Cursor, Copilot) to ship higher-quality software faster\nExperience with FastAPI (or Flask/Django) for building Python web services and APIs\nExperience with React or another modern front-end framework for building richer UIs beyond Streamlit\nProficiency with data visualization libraries (Plotly, Matplotlib, Seaborn) and an eye for usable, well-designed UIs\nSolid grasp of data warehousing concepts and ETL/ELT design\nExperience leading projects or mentoring engineers in a senior IC capacity\nExperience shipping LLM-powered features to production (not just prototypes)\nExperience with cloud platforms (AWS, Azure, Google Cloud Platform) and containerization (Docker, Kubernetes)\nBackground working with Finance, FP&A, or Sales Finance teams\nStrong communication skills with the ability to translate between technical and business contexts