Engineering Manager, App Store Data

Apple Apple · Big Tech · San Francisco, CA · Software and Services

Engineering Manager for App Store's data engineering and analytics team, focusing on compliance-critical data products, pipelines, and analytical outputs. The role involves people leadership, technical guidance, and ensuring operational excellence while partnering with stakeholders to deliver scalable data solutions. Emphasizes AI-native development practices and LLM-augmented workflows across the SDLC, with a strong focus on data quality, governance, and compliance.

What you'd actually do

  1. 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 leadership
  2. Establish and enforce standards for metrics, data governance, documentation, and SLAs, overseeing data quality with proactive issue resolution through alerting and root-cause analysis
  3. Build, coach, and elevate a high-performing analytics engineering team, setting clear goals and fostering a culture of inclusion, learning, and operational excellence
  4. Collaborate closely with product and engineering to prioritize the analytics engineering roadmap and drive data-driven decision-making
  5. Scale data self-service and literacy by expanding documentation, training, and enablement in key tools and dashboards, empowering stakeholders to explore data independently

Skills

Required

  • Analytics engineering
  • Data engineering
  • Analytics
  • SQL
  • Python
  • Java
  • Scala
  • Data modeling
  • People leadership
  • Hiring
  • Coaching
  • Performance management
  • Semantic layers
  • Scalable dashboards
  • Data pipelines
  • Validation
  • Monitoring
  • Data quality
  • Communication
  • Cross-functional stakeholder partnership
  • KPI definition
  • Project end-to-end delivery
  • AI coding assistants
  • LLM-augmented workflows
  • Bachelor's Degree in Computer Science, Engineering or a Quantitative discipline

Nice to have

  • Trino
  • Superset
  • BI/visualization tools
  • Regulatory and Compliance engineering
  • GDPR operational requirements
  • data minimization
  • DMA
  • DSA obligations
  • Privacy-first data architecture

What the JD emphasized

  • compliance-critical data products
  • regulatory and privacy obligations
  • correctness of compliance data products
  • team adherence to compliance controls
  • AI-native development is a standard operating mode
  • LLM-augmented workflows
  • compliance-critical contexts

Other signals

  • AI-native development is a standard operating mode
  • Guide the team's adoption of LLM-augmented workflows
  • Maintain a disciplined approach to AI tool output
  • Active use of AI coding assistants and LLM-augmented workflows