Platform & Data Engineer

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. 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. 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.