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.

DESCRIPTION

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

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.