People Analytics Full Stack Developer

Apple Inc.
Cupertino, CA, United States
5 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
7 years minimum
Working hours
Regular working hours
Job source

Tech stack

Application Programming Interfaces (APIs) Business Analytics Applications Extract Transform Load (ETL) Data Presentation Data Security Data Sharing Linux File Systems HR Analytics Python (Programming Language) NumPy Reverse Proxy
+15 more
Software Engineering SQL Databases Web Application Frameworks Web Services Data Processing Flask (Web Framework) Claude Code Snowflake Caching Backend Fastapi Pandas Build Management Information Technology Model Context Protocol

Job description

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.

Requirements

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.

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