People Analytics Full Stack Developer

At Apple, our greatest resource is our people. The People Analytics team builds the

data products that help Apple's HR organization make decisions with evidence:

measuring how we recruit, develop, listen to and retain employees, and putting that

insight in front of the teams and leaders who act on it. The work is small-team and

high-ownership - the person who models the data is the same person who ships the

dashboard and operates it in production.

Description

This role builds and runs analytics products end to end: modeling data in Snowflake,

developing Python web services and APIs, building dashboards that surface actionable

insight, and automating deployment across Linux infrastructure. You will use agentic

AI coding tools such as Claude Code as a primary means of delivery, running parallel

sessions to design, build, test and ship - while holding the standards that generated

code does not: sound architecture, data security, and catching the query that runs

without error and returns the wrong number.

Minimum Qualifications

Bachelor's degree in Computer Science, Information Management Systems, Data Science,

Software Engineering, or a related field.

7+ years of experience developing and maintaining analytics products, reports and

dashboards, including dashboard visualization development.

Deep SQL and Snowflake experience: designing schemas, optimizing queries, and

building ETL pipelines including incremental refresh and caching strategies.

Python experience spanning backend web services, APIs, and data-processing

automation.

Hands-on Linux experience operating servers unaided: working over SSH, running

long-lived services behind a reverse proxy, and diagnosing problems with processes,

networking, file systems and performance.

Container experience covering image build and deployment, and troubleshooting

networking, storage and runtime issues.

Daily production experience with agentic AI coding tools such as Claude Code,

including running parallel sessions and reviewing generated code to identify edge

cases, incorrect output and unsound patterns before it ships.

A track record of confirming data and system behavior by measuring against the live

system rather than inferring it from documentation, naming or generated explanations.

Experience working with employee data or other sensitive personal data under

row-level security and data-access restrictions.

Proven autonomy: experience owning delivery end to end with minimal direction,

choosing the approach and making implementation decisions without escalation.

Experience delivering to competing deadlines and shifting priorities without loss of

data accuracy, and setting expectations with stakeholders on scope and timing.

Flexibility to work across time zones, including meetings outside standard hours to

reach colleagues and partners in other regions.

Preferred Qualifications

Experience agreeing metric definitions with business partners and holding those

definitions consistent across multiple reporting surfaces.

Experience delivering on a shared data platform where pipeline changes are

centrally owned: scoping a minimal change, evidencing it, and sequencing configuration and code releases.

Experience working directly with senior business leaders: taking requirements

first-hand, presenting data and findings, explaining caveats clearly to

non-technical partners, and responding when the numbers are challenged.

Familiarity with Python web frameworks such as Flask or FastAPI.

Experience with Python data processing libraries such as NumPy and pandas, and

awareness of data science and statistical analysis techniques.

Experience building or operating services that make internal systems available to

AI tooling, such as Model Context Protocol (MCP) servers.