Analytics Engineer - Supply Chain (Business Process Re-engineering)

Imagine what you could do here. At Apple, great ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.

Description
Our BPR team is looking for an Analytics Engineer who combines deep supply chain functional expertise with strong technical depth to help transform how Apple's Worldwide Operations organization uses data, analytics, and AI to run and improve the supply chain.

You'll design and build the data models, semantic layers, and analytics products that power decisions across Planning, Operations, Logistics, Manufacturing, and Fulfillment. This is a highly visible role - partnering with business leaders and technology teams to shape Apple's supply chain data and analytics landscape.","responsibilities":"Design and build large-scale, performant analytical data models and semantic layers for one of Apple's most complex supply chain data ecosystems.
Partner with Supply Chain, Operations, Finance, and Logistics teams to translate business needs into analytics solutions that drive measurable outcomes.
Own the semantic and metric layer that becomes the single source of truth for supply chain KPIs - demand attainment, service level, inventory position, lead time, on-time delivery, freight cost, and more.
Drive AI-readiness of supply chain data - high-quality, governed, discoverable datasets that power dashboards, ML models, and GenAI applications.
Lead end-to-end delivery - from requirements and design to production, across multiple concurrent projects.
Collaborate with technology teams on platform capabilities, tooling, data governance, and master data.

Preferred Qualifications
Exposure to NPI processes and how supply chain flows change through launches and transitions.
Strong analytics engineering skills - dbt (or equivalent), Git, CI/CD for analytics, testing frameworks.
Strong data modeling - dimensional / star schema, semantic layer design.
Advanced Tableau (visualization skills) - dashboard design, performance tuning, publishing.
Python for data manipulation, automation, and lightweight app building.
Workflow orchestration (Airflow or equivalent) and data quality frameworks.
Understanding of GenAI-readiness - how semantic models enable NL-to-SQL, RAG, and conversational analytics.

Minimum Qualifications
8-12 years working with supply chain / operations data - Planning, Forecasting, Order Management, Inventory, Logistics, or Manufacturing.
Deep understanding of end-to-end supply chain processes (S&OP, demand/supply planning, inventory management, transportation) and their core metrics.
Expert SQL and hands-on experience with modern cloud data warehouses - Snowflake, BigQuery, or SingleStore
Bachelor's in Computer Science, Industrial Engineering, Operations Research, Supply Chain Management, Statistics, or a related field. Master's preferred. Advanced analytics, ML, or GenAI knowledge is a strong plus.