Job Description
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.
Preferred Qualifications
Hands-on experience with production observability systems — error tracking, log aggregation, understanding how to keep on-call sustainable.
Solid experience designing or contributing to APIs, ideally with exposure to versioning, multi-tenancy, authentication, or capacity planning at scale.
Experience building or maintaining ETL / data pipelines, including ingestion, transformation, and reliability considerations.
Strong product instinct: you have built tooling that other engineers actually adopt, and you can reason about the user, not just the query plan.
Prior experience building or contributing to self-service data products — such as query builders, templates, or interfaces designed for non-experts.
Exposure to LLM-assisted query construction or tooling built on Model Context Protocol (MCP) or similar wrappers.
Interest in or experience evolving a platform from curated partnerships toward self-service as adoption patterns mature.
A bias toward sustainable operations: you understand the value of replacing heroics with systems, and prefer instrumentation over guesswork.
Comfort with or interest in working in a small team where ownership is broad and the line between "platform" and "product" is intentionally blurry.
Minimum Qualifications
Bachelor's degree in Computer Science or a related field, or equivalent practical experience.
3-5 years of professional software engineering experience (or equivalent), with hands-on time operating production systems in at least one of: Kubernetes, large-scale data systems, or internal platform infrastructure.
Deep, hands-on production experience operating Kubernetes at scale, including scaling, debugging, and operating clusters under real load, with a track record of improving scalability and debuggability of large clusters.
Experience with MongoDB or similar document databases, with familiarity of aggregation patterns and practices for maintaining performance at scale; deep knowledge of the aggregation pipeline is a plus but not required.
Professional fluency in Python, and comfort owning code in production.
Experience navigating and building within large-scale internal infrastructure environments.
