People Analytics Full Stack Developer

Apple, Inc.; Cork; On-site; EUR 90,000 - 130,000; Full time

Job summary

Apple is seeking a People Analytics Full Stack Developer in Cork, Ireland to build end-to-end analytics products. You will model data in Snowflake, develop Python backends and APIs, and create dashboards powering HR decisions. Expect to run ETL pipelines, automate deployments on Linux, and work with agentic AI tools in a high-ownership, collaborative environment.

You will deliver scalable data solutions, ensure data quality and privacy, and partner with global teams to meet executive needs.

Qualifications

• Deep SQL and Snowflake experience required.
• Python backend and data-processing automation experience.
• Linux server ops, SSH, reverse proxy, performance troubleshooting.
• Containerization and deployment experience.
• Daily use of agentic AI coding tools for development.
• Data validation against live systems is essential.
• Experience building analytics, dashboards, and reports.
• Handling sensitive employee data with privacy controls.
• Autonomy delivering end-to-end projects with minimal guidance.
• Ability to align with global stakeholders and meet deadlines.
• Communicating clearly with senior leaders and partners.

Responsibilities

• Design, develop and deploy dashboards and analytics across the employee lifecycle.
• Build data models, schemas and views in Snowflake for reliable analytics.
• Develop Python backends, APIs and frontends powering internal tools.
• Create ETL pipelines with Snowflake to operational databases.
• Automate build, containerization, deployment and config management on Linux.
• Deliver work using AI coding tools while ensuring correctness and security.
• Define metrics and ensure consistency across reporting surfaces.
• Maintain documentation and context for large reporting estates.
• Re-engineer analytics systems to improve simplicity, security and new features.
• Collaborate with People Analytics and technology teams to deliver solutions.
• Respond directly to senior leaders with data insights and recommendations.

Skills

SQL & Snowflake

Python backend

Linux servers

Containerization

Agentic AI tools

Data accuracy & validation

Analytics products

Data privacy

Delivery autonomy

Stakeholder communication

Education

Bachelor's or Master's in CS/IS/Data Science

Tools

Snowflake

Docker

Flask/FastAPI

Claude Code

Job description

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People Analytics Full Stack Developer

Cork, County Cork, Ireland Corporate Functions

At Apple, our greatest resource is our people. The People Analytics team builds thedata products that help Apple's HR organization make decisions with evidence:measuring how we recruit, develop, listen to and retain employees, and putting thatinsight in front of the teams and leaders who act on it. The work is small-team andhigh-ownership - the person who models the data is the same person who ships thedashboard and operates it in production.Imagine what you could do at Apple.

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 actionableinsight, and automating deployment across Linux infrastructure. You will use agenticAI coding tools such as Claude Code as a primary means of delivery, running parallelsessions to design, build, test and ship - while holding the standards that generatedcode does not: sound architecture, data security, and catching the query that runswithout error and returns the wrong number.

Responsibilities

• Design, develop and deploy reports, dashboards and analytics across the employee lifecycle, enabling data-driven decisions for business partners and leadership.
• Build and optimize data models, schemas and views in Snowflake and relational databases to support reliable, high-quality analytics products.
• Develop and maintain Python backends, APIs and web frontends that power internal people tools and employee-facing applications.
• Build and operate ETL and data-refresh pipelines between Snowflake and operational databases, including scheduling, incremental refresh, caching and data-integrity validation.
• Automate build, containerization, deployment and configuration management across Linux-based infrastructure.
• Deliver engineering work with agentic AI coding tools as the primary development method: scoping and decomposing tasks so several sessions can run in parallel, then reviewing and correcting the generated code before it ships.
• Establish and own metric definitions: determine how each measure is calculated, keep the same metric reporting identical wherever it appears, and validate figures against source data before release.
• Maintain the technical documentation and codebase context that keep a large reporting estate workable, for colleagues and for AI tooling alike.
• Re-engineer existing analytics and reporting systems to improve simplicity, standardization and security, alongside delivering new features.
• Partner with People Analytics colleagues and internal technology teams to deliver analytics solutions.
• Responding to senior business leaders directly on analytical questions, providing data insights and recommendations to support decision making.

Minimum Qualifications

• 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 independently: 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, as well as 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 shipping.
• A track record of confirming data and system behavior by measuring against the live system rather than inferring it from documentation, naming conventions, or generated explanations.
• Experience developing and maintaining analytics products, reports, and dashboards, including dashboard visualization development.
• Experience handling employee data or other sensitive personal data under row-level security and data-access restrictions, with a strong understanding of legal data privacy responsibilities, duty of care, and accountability.
• Proven autonomy: experience owning delivery end-to-end with minimal direction, choosing the approach, and making implementation decisions independently.
• Experience delivering to competing deadlines and shifting priorities without loss of data accuracy, while proactively setting expectations with stakeholders on scope and timing.
• Ability to partner effectively with colleagues and partners across different global regions.
• Ability to operate at a senior leader/executive level within the organization, providing data clarity and actionable insights.

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Information Management Systems, Data Science, Software Engineering or a related field; or equivalent professional experience.
• 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.
• 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 agreeing on metric definitions with business partners and holding those definitions consistent across multiple reporting surfaces.
• 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.

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