At Apple, our greatest resource is our people, and the People Analytics Team is dedicated to ensuring Apple’s employees are able to do the best work of their lives.
The People Analytics team is looking for a versatile, self-driven software engineer who has hands-on depth across the full stack, to craft, implement and operate analytics solutions that have direct and measurable impact to the teams that support our people.
This is a rare opportunity to be part of a dynamic team delivering analytics capabilities across the people domain, in the areas of recruitment, employee relations, people survey, talent planning and a number of other strategic focus areas for the organisation.
As a professional who combines deep analytics capability with full-stack software engineering, working with a diverse team of experts, imagine what you could do at Apple.
As an analytics developer and engineer, you will play a pivotal role in the People Analytics team, working with data in the Apple enterprise data warehouse from multiple systems across the Apple HRIS landscape to derive powerful insights for leadership and build analytical products which drive a positive employee experience across Apple.
You work across the full spectrum - from modelling data in Snowflake and building dashboards that surface actionable insight, to developing Python-backed web services and automating workflows with Bash across Linux-based systems. You own solutions across the data, backend, frontend, API, and infrastructure layers - including the containers, daemons, ETL pipelines and internal Model Context Protocol (MCP) servers that power AI and system interoperability, applying best practices and sound technical judgement with a high degree of autonomy.
Minimum Qualifications
- 10+ years of professional experience developing and maintaining analytics products and reports, with proficiency in dashboard visualisation development.
- Strong data engineering skills including deep SQL expertise to develop optimised data sets across analytical and operational products.
- Good knowledge of Snowflake enterprise data warehouse technology and relational database technologies such as MySQL and PostgreSQL, including schema design and query optimisation.
- Proven ETL / data pipeline engineering: designing and operating extract-transform-load workflows across Snowflake and relational databases, including scheduling, incremental/near-real-time refresh, and caching strategies.
- Deep proficiency in Python - applied both to backend web development and to data processing and automation tasks.
- Solid Linux systems proficiency - Solid Linux systems proficiency - comfortable operating over SSH, managing long-running services and daemons (systemd, gunicorn/uwsgi, nginx/reverse proxies), and troubleshooting at the systems level including processes, networking, file systems, and performance diagnostics, unaided.
- Hands-on experience with containers (Docker or an equivalent OCI runtime): authoring Dockerfiles, building and deploying images, and troubleshooting container networking, storage, and runtime issues.
- Demonstrated proficiency in Bash scripting for automation, system administration, and operational integration tasks.
- Able to design and deliver a feature across every layer - data model, backend, API, and user interface - and make sound design trade-offs with minimal guidance.
- Active team player with strong collaboration skills and the ability to partner with teams globally through consistent and effective communication across time zones.
- Comfortable managing multiple priorities, setting stakeholder expectations appropriately, and proactively assessing incoming work to take the right action.
Preferred Qualifications
- B.S. or M.S. in Computer Science, Information Management Systems, Data Science, Software Engineering, or a related field; equivalent professional experience will be fully considered.
- Familiarity with Python web frameworks such as Flask or FastAPI.
- Experience working with employee data and HR systems.
- Knowledge of more advanced data processing techniques using Python libraries such as NumPy and Pandas, as well as awareness of data science and statistical analysis libraries.
- Experience building or operating internal Model Context Protocol (MCP) servers or comparable AI/system interoperability services.
- Experience with AI/ML integration - LLM APIs, RAG architectures, or AI-assisted tooling, and curiosity about applying these capabilities to analytics and employee experience products.
- Experience working across multiple geographies and cultures within a global engineering organisation.