Platform & Data Engineer

Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.

We are looking for a Platform & Data Engineer to own the systems that thousands of internal engineers rely on every day. This is a rare, broad role: you will operate at the intersection of Kubernetes platform engineering and large-scale data engineering, owning both the compute platform our internal tools run on and the data layer that makes them useful. You will not just keep these systems healthy — you will build the products and interfaces that let other teams move faster. If you are excited by ambiguity, take real ownership, and want your work to be felt across the company, we'd love to talk.

You will be a foundational member of a small, high-trust team that builds and operates the platform behind Apple's internal automation and testing infrastructure. The role spans two deeply connected domains, and we expect genuine strength in both.

On the platform side, you will lead the scalability and debuggability of our Kubernetes footprint at Apple-internal scale. You will take ownership of our observability stack — currently maintained on a volunteer basis — and put it on durable footing, including end-to-end error tracking and log aggregation across services. You will design and build internal-tools APIs that hold up under real load, partnering with teams on versioning, multi-tenancy, authentication, and capacity planning. You will also shape the adopter-facing surface of the platform: today that means working closely with the teams who depend on us; over time, as patterns stabilize, it means collaborating on the SDK and self-service experience that lets the next wave of teams onboard themselves.

On the data side, you will lead our MongoDB estate ingesting millions of records per day and growing. You will be responsible for query optimization, indexing strategy, and sharding as the dataset scales, working with data teams on these decisions. You will own and improve the ETL pipelines that feed it. And — this is the part that distinguishes a builder from a DBA — you will design and ship the self-service query layer that lets client teams answer their own aggregation questions instead of routing one-off requests through chat. You will be designing user-facing tooling, so product instinct matters as much as performance tuning.

We have early building blocks in place, including MCP wrappers you can build on, and we are genuinely interested in candidates who have explored query builders, query templates, or LLM-assisted query construction. We care about people who are unusually thoughtful about the systems they build, who default to ownership, and who can move between a deep performance problem and a user-facing design decision in the same afternoon.

Minimum Qualifications

1+ years of professional software engineering experience, including internships or co-ops.
Proficiency in Python, and experience with at least one other language (such as Go, Java, or C++).
Experience with relational or document databases and writing non-trivial queries.
Experience building and shipping a backend service or API.
Strong computer science fundamentals, including operating systems, databases, and distributed systems.
Willingness to learn Kubernetes and MongoDB in a production environment.
Bachelor’s degree in Computer Science or a related field, or equivalent practical experience.

Preferred Qualifications

Prior experience with Apple internal data platforms or tooling, including through an internship.
Hands-on exposure to Kubernetes or cloud platforms (AWS, GCP), through work, coursework, or personal projects.
Experience with MongoDB, including the aggregation pipeline.
Prior experience building self-service data products — query builders, query templates, or interactive query interfaces that non-experts can use safely.
Exposure to LLM-assisted query construction or tooling built on Model Context Protocol (MCP) or similar wrappers.
Experience building observability or monitoring tooling, such as tracking latency, resource contention, or error rates.
Experience working in a small team where ownership is broad and the line between “platform” and “product” is intentionally blurry.