Engineering Leader, Data Engineering

Apple Apple · Big Tech · Seattle, WA +2 · Software and Services

Engineering leader to build and lead a data engineering organization responsible for massive-scale data pipelines and services for Apple's Media Services. The role involves architecting systems for an AI-first paradigm, producing ML-ready data, and leading technology modernization programs.

What you'd actually do

  1. Lead a large data engineering organization, setting technical direction, defining team structure, and building a culture of ownership, craft, and continuous improvement.
  2. Oversee the design, development, and operation of massive-scale data pipelines and services that support commerce, payments, and content domains for Apple's Media Services products.
  3. Drive technology modernization programs, leading the shift toward an AI-first data architecture and executing large-scale migrations with minimal disruption to downstream consumers.
  4. Partner with engineering, product, and applied research teams across Apple to align platform capabilities with the needs of customer-facing features, internal reporting, and external partner integrations.
  5. Recruit, develop, and retain exceptional engineering talent, empowering individuals to expand their technical reach and growing future leaders.

Skills

Required

  • 10+ years of engineering experience
  • 5+ years leading large engineering organizations (50+ engineers) with multiple layers of management
  • Proven track record of recruiting, developing leadership talent, and building high-performing engineering cultures at scale
  • Deep architectural expertise in large-scale data systems
  • Demonstrated success planning and executing large-scale technology modernization or platform migration programs
  • Ability to shift seamlessly between deep technical details and big-picture strategy
  • Experience leading geographically distributed engineering teams across multiple time zones
  • Exceptional communication skills
  • Strong cross-functional collaboration skills

Nice to have

  • Hands-on experience with large-scale data processing and streaming technologies such as Spark, Flink, Kafka, Apache Iceberg, and Snowflake
  • Background in AI and machine learning applications within data engineering contexts, such as feature pipelines, model serving infrastructure, or ML platform development
  • Experience operating data platforms at consumer-internet scale
  • Background in commerce, payments, digital content, or media domains
  • Familiarity with Apple's privacy principles or equivalent experience designing data systems with privacy-by-default architectures
  • History of driving cross-organizational data platform standards or governance programs adopted across multiple engineering teams

What the JD emphasized

  • AI-first paradigm
  • ML-ready data
  • massive-scale data pipelines and services

Other signals

  • architecting systems that produce the ML-ready data necessary to fuel machine learning applications at scale
  • shifting from traditional data pipelines to an AI-first paradigm
  • massive-scale data pipelines and services