Data Engineer (Customer Analytics), IS&T Ai & Data Platforms
Apple · Shanghai ·
- Category
- Data engineering
- Experience
- 3+ years
- Company size
- 1000+
Apple · Shanghai ·
Build and optimize cloud data warehouses and ETL pipelines using Spark, Trino, and Python to power AI-driven analytics across Apple’s global teams.
AI & Data Platforms (AiDP) is IS&T's engine for AI-powered innovation. The team brings together data, application development, and machine learning — including generative AI — along with data services and customer success functions, to help IS&T build solutions more efficiently and streamline the adoption and embedding of generative AI across Apple.
Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your work, and there's no telling what you could accomplish.
AI & Data Platforms (AiDP) is IS&T's engine for AI-powered innovation. The team brings together data, application development, and machine learning — including generative AI — along with data services and customer success functions, to help IS&T build solutions more efficiently and streamline the adoption and embedding of generative AI across Apple. As a Senior Data Engineer on the team, you'll design and build the data warehouses, pipelines, and analytics that power decisions across Apple, working closely with teams in the US and around the world.
Design, build, and manage cloud-based data warehouses and data access layers that support teams across Apple. Develop highly scalable and secure ETL pipelines to ingest, process, and serve data from a multitude of source systems. Perform data discovery and in-depth analysis across large, multi-source datasets, quickly learning new data to uncover insights and validate quality. Build proofs of concept and run ad-hoc and recurring analyses to answer high-priority business questions. Analyze and optimize existing data systems and queries, including Spark and Trino, for reliability, performance, and cost at scale. Use modern AI tools to enhance and automate data workflows such as orchestration, scheduling, and monitoring. Collaborate closely with US-based and regional teams and business partners, presenting findings and recommendations in English.