Engineering Manager, App Store Data

Summary

Apple's App Store serves half a billion customers every week across every Apple device, and has fundamentally changed how people discover, access, and build software since 2008. It is central to Apple's business and to the global developer ecosystem. Building and leading the data team behind it means operating at a level of scale, accountability, and organizational influence that few engineering leaders encounter.

Description

We are seeking an Engineering Manager to lead App Store’s data engineering and analytics team. This team owns compliance-critical data products: pipelines and analytical outputs subject to regulatory and privacy obligations, as well as analytics engineering, EDA, ML-based pipeline health monitoring, and self-serve data products.

The EM is accountable not just for delivery, but for the correctness of compliance data products and team adherence to compliance controls. This role blends technical depth with people leadership - you’ll lead with empathy and energy, guide engineering execution, ensure operational excellence, and stay hands-on in key parts of our data ecosystem. You will partner closely with stakeholders across the App Store business to translate strategic priorities into scalable data solutions, sustainable delivery practices, and a strong culture of ownership and growth.

AI-native development is a standard operating mode with EM expected to model and champion AI-native engineering practices across the full SDLC.

Preferred Qualifications

Experience with Regulatory and Compliance engineering - GDPR operational requirements, data minimization, DMA, DSA obligations, Privacy-first data architecture at a technical level.
Experience working with privacy, legal and regulatory stakeholders in an engineering capacity

Minimum Qualifications

7+ years in analytics engineering, data engineering, or analytics, with strong hands‑on SQL, Python or Java, Scala and data modeling expertise
3+ years of people leadership experience, including hiring, coaching, and performance management in analytics/data teams
Proven ability to define semantic layers and build scalable dashboards; experience with Trino, Superset or other BI/visualization tools.
Experience building and operating data pipelines with robust validation and monitoring; high bar for data quality
Excellent communication; proven success partnering with cross‑functional stakeholders (Product, Engineering, Legal, Privacy etc.) to define KPIs and deliver measurable outcomes
Track record of driving projects end‑to‑end and demonstrating clear outcomes and impact
Active use of AI coding assistants and LLM-augmented workflows in a professional engineering context and models AI-native practices for the team
Bachelor's Degree in Computer Science, Engineering or a Quantitative discipline (e.g., Statistics, Economics, Mathematics)