People Analytics Full Stack Developer

At Apple our greatest resource is our people. The People Analytics team builds thendata products that help Apples HR organization make decisions with evidence:nmeasuring how we recruit develop listen to and retain employees and putting thatninsight in front of the teams and leaders who act on it. The work is small-team andnhigh-ownership - the person who models the data is the same person who ships thendashboard and operates it in

This role builds and runs analytics products end to end: modeling data in Snowflakendeveloping Python web services and APIs building dashboards that surface actionableninsight and automating deployment across Linux infrastructure. You will use agenticnAI coding tools such as Claude Code as a primary means of delivery running parallelnsessions to design build test and ship - while holding the standards that generatedncode does not: sound architecture data security and catching the query that runsnwithout error and returns the wrong number.

Design develop and deploy reports dashboards and analytics across the employeenlifecycle enabling data-driven decisions for business partners and and optimize data models schemas and views in Snowflake and relationalndatabases to support reliable high-quality analytics and maintain Python backends APIs and web frontends that power internalnpeople tools and employee-facing and operate ETL and data-refresh pipelines between Snowflake and operationalndatabases including scheduling incremental refresh caching and build containerization deployment and configuration management acrossnLinux-based engineering work with agentic AI coding tools as the primary developmentnmethod: scoping and decomposing tasks so several sessions can run in parallel thennreviewing and correcting the generated code before it and own metric definitions: determine how each measure is calculatednkeep the same metric reporting identically wherever it appears and validatenfigures against source data before the technical documentation and codebase context that keep a largenreporting estate workable for colleagues and for AI tooling -engineer existing analytics and reporting systems to improve simplicitynstandardization and security alongside delivering new with People Analytics colleagues and internal technology teams to delivernanalytics role works with colleagues in other regions so occasional meetings outsidenstandard working hours should be expected. Regular travel is not required.n

Bachelors degree in Computer Science Information Management Systems Data SciencenSoftware Engineering or a related 7 years of experience developing and maintaining analytics products reports andndashboards including dashboard visualization SQL and Snowflake experience: designing schemas optimizing queries andnbuilding ETL pipelines including incremental refresh and caching experience spanning backend web services APIs and -on Linux experience operating servers unaided: working over SSH runningnlong-lived services behind a reverse proxy and diagnosing problems with processesnnetworking file systems and experience covering image build and deployment and troubleshootingnnetworking storage and runtime production experience with agentic AI coding tools such as Claude Codenincluding running parallel sessions and reviewing generated code to identify edgencases incorrect output and unsound patterns before it track record of confirming data and system behavior by measuring against the livensystem rather than inferring it from documentation naming or generated working with employee data or other sensitive personal data undernrow-level security and data-access autonomy: experience owning delivery end to end with minimal directionnchoosing the approach and making implementation decisions without delivering to competing deadlines and shifting priorities without loss ofndata accuracy and setting expectations with stakeholders on scope and to work across time zones including meetings outside standard hours tonreach colleagues and partners in other regions.n

Experience agreeing metric definitions with business partners and holding thosendefinitions consistent across multiple reporting delivering on a shared data platform where pipeline changes arencentrally owned: scoping a minimal change evidencing it and sequencing configuration and code working directly with senior business leaders: taking requirementsnfirst-hand presenting data and findings explaining caveats clearly tonnon-technical partners and responding when the numbers are with Python web frameworks such as Flask or with Python data processing libraries such as NumPy and pandas andn awareness of data science and statistical analysis building or operating services that make internal systems available tonAI tooling such as Model Context Protocol (MCP) servers.