Engineering Manager, App Store Data

Apples 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 Apples 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.

We are seeking an Engineering Manager to lead App Stores 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 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 - youll 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 -native development is a standard operating mode with EM expected to model and champion AI-native engineering practices across the full SDLC.n

Develop and own the vision for Analytics Engineering delivering curated datasets dashboards and internal data products. Define outcomes and communicate prioritization progress and impact to leadershipnEstablish and enforce standards for metrics data governance documentation and SLAs overseeing data quality with proactive issue resolution through alerting and root-cause analysisnBuild coach and elevate a high-performing analytics engineering team setting clear goals and fostering a culture of inclusion learning and operational excellencenCollaborate closely with product and engineering to prioritize the analytics engineering roadmap and drive data-driven decision-makingnScale data self-service and literacy by expanding documentation training and enablement in key tools and dashboards empowering stakeholders to explore data independentlynGuide architectural and operational excellence across data pipelines championing best practices in code review and version control for enhanced reliabilitynBuild in-person relationships with team members and contribute to Apples company culturenGuide the teams adoption of LLM-augmented workflows across requirements analysis pipeline generation test coverage documentation and incident triagenMaintain a disciplined approach to AI tool output especially in compliance-critical contexts where AI-generated code requires the same review rigor as human-authored code

7 years in analytics engineering data engineering or analytics with strong handson SQL Python or Java Scala and data modeling expertisen3 years of people leadership experience including hiring coaching and performance management in analytics/data teamsnProven ability to define semantic layers and build scalable dashboards; experience with Trino Superset or other BI/visualization building and operating data pipelines with robust validation and monitoring; high bar for data qualitynExcellent communication; proven success partnering with crossfunctional stakeholders (Product Engineering Legal Privacy etc.) to define KPIs and deliver measurable outcomesnTrack record of driving projects endtoend and demonstrating clear outcomes and impactnActive use of AI coding assistants and LLM-augmented workflows in a professional engineering context and models AI-native practices for the teamnBachelors Degree in Computer Science Engineering or a Quantitative discipline (e.g. Statistics Economics Mathematics)

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