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 theres no telling what you could are looking for a Platform u0026 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 wed love to talk.

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

Bachelors degree in Computer Science or a related field or equivalent practical experience.n3-5 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 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 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 fluency in Python and comfort owning code in navigating and building within large-scale internal infrastructure environments.

Hands-on experience with production observability systems error tracking log aggregation understanding how to keep on-call experience designing or contributing to APIs ideally with exposure to versioning multi-tenancy authentication or capacity planning at building or maintaining ETL / data pipelines including ingestion transformation and reliability product instinct: you have built tooling that other engineers actually adopt and you can reason about the user not just the query experience building or contributing to self-service data products such as query builders templates or interfaces designed for to LLM-assisted query construction or tooling built on Model Context Protocol (MCP) or similar in or experience evolving a platform from curated partnerships toward self-service as adoption patterns bias toward sustainable operations: you understand the value of replacing heroics with systems and prefer instrumentation over with or interest in working in a small team where ownership is broad and the line between platform and product is intentionally blurry.