Job description

As a data engineering leader within Media Services, you will direct a large organization responsible for the design, development, and operation of massive-scale data pipelines and services across Apple's commerce, payments, and content domains. Your team's work powers customer-facing features like search and personalization, as well as critical reporting and analytics for internal and external partners, all built to Apple's exacting standards for reliability, quality, and privacy. Crucially, you will lead the team through a critical evolution: shifting from traditional data pipelines to an AI-first paradigm, architecting systems that produce the ML-ready data necessary to fuel machine learning applications at scale. This is a deeply cross-functional leadership role. You will collaborate with engineering and business organizations across Apple, influence platform strategy at the executive level, and build a high-performing culture where engineers are empowered to deepen their expertise and extend their impact across the organization. 10+ years of engineering experience, including 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, with strong instincts for technical excellence and software engineering best practices. 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, with the judgment to know when each is required. Experience leading geographically distributed engineering teams across multiple time zones. Exceptional communication skills, able to drive clarity in ambiguity, distill complex concepts concisely, and effectively engage stakeholders at all levels. Strong cross-functional collaboration skills, with a track record of influencing outcomes across diverse engineering organizations without direct authority. 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, with a deep appreciation for the intersection of data quality, privacy, and user-facing impact. 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.

Requirements

Must-haves

10+ years of engineering experience, including 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, with strong instincts for technical excellence and software engineering best practices.
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, with the judgment to know when each is required.
Experience leading geographically distributed engineering teams across multiple time zones.
Exceptional communication skills, able to drive clarity in ambiguity, distill complex concepts concisely, and effectively engage stakeholders at all levels.
Strong cross-functional collaboration skills, with a track record of influencing outcomes across diverse engineering organizations without direct authority.
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, with a deep appreciation for the intersection of data quality, privacy, and user-facing impact.
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learning

Platform: apple
Effort: medium