Data Applications Engineer

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. Apple is seeking a 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.

Minimum Qualifications

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

Preferred Qualifications

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 Demonstrated use of AI-assisted development tools (e.g., Claude Code, Cursor, Copilot) to ship higher-quality software faster Experience with FastAPI (or Flask/Django) for building Python web services and APIs Experience with React or another modern front-end framework for building richer UIs beyond Streamlit Proficiency with data visualization libraries (Plotly, Matplotlib, Seaborn) and an eye for usable, well-designed UIs Solid grasp of data warehousing concepts and ETL/ELT design Experience leading projects or mentoring engineers in a senior IC capacity Experience shipping LLM-powered features to production (not just prototypes) Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes) Background working with Finance, FP&A, or Sales Finance teams Strong communication skills with the ability to translate between technical and business contexts

Description

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. You 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. Success 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.

Responsibilities

Own complex projects end-to-end with minimal direction — from discovery and scoping through design, development, deployment, and long-term maintenance Partner with financial analysts and Finance leadership to deeply understand workflows and pain points, and proactively identify high-leverage opportunities for automation and AI Design, develop, and deploy data applications (primarily Streamlit) that automate tasks, visualize data, and improve the efficiency of financial processes Lead the team’s adoption of AI — both in our own engineering practice (AI-assisted development, agentic tooling) and in the products we ship (LLM-powered features, RAG, agents, intelligent automation) Coach financial analysts on where and how to apply AI in their own workflows, helping them separate hype from real opportunity and prototype solutions together Write clean, well-documented, maintainable Python — and establish patterns and reusable components others on the team can build on Connect applications to a variety of data sources (databases, APIs, data lakes) across Apple’s data ecosystem Architect for reliability: robust error handling, logging, monitoring, CI/CD, and security-by-default Gather user feedback and iterate to continuously improve usability and impact Partner with data scientists to integrate analytical and ML models into applications Set technical standards and mentor other engineers on the team Document applications, data flows, architecture decisions, and technical specifications