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.

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.

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.