Senior Data Engineer — Music Services Operations Analytics & Strategy

Apple•Cupertino, CA

About The Position

The Music Services Operations Analytics & Strategy team builds the intelligence infrastructure that powers Apple's media services. Processing billions of records monthly, we manage complex content taxonomies, variable provider data, and hundreds of analytical dimensions — delivering accurate, actionable insights to cross-functional teams, partner organizations, and executive leadership. We are seeking a Senior Data Engineer to architect and deploy the robust, AI-ready data pipelines that serve as the foundation for our analytics capabilities. This role demands both deep systems engineering rigor and strong business partnership — the ideal candidate brings battle-tested experience at massive scale and the strategic foresight to build extensible, modular systems that span across Music, Video, and Books.

Requirements

7+ years of professional experience in data engineering or systems architecture.
Demonstrated history of owning production data systems at massive scale — processing billions of records in complex, high-volume environments.
Advanced proficiency in Python and SQL.
Expertise in distributed data processing frameworks (e.g., PySpark/Spark).
Expertise in modular software design.
Expertise in automated testing methodologies.
Extensive hands-on experience designing and operating CI/CD pipelines, automated deployment workflows, and version-controlled data infrastructure.
Proven expertise in data quality management.
Proven expertise in resolving complex taxonomy mapping issues.
Proven ability to lead projects, influence cross-functional teams, and drive consensus in a matrixed organization.
Exceptional written and verbal communication skills.
Exceptional aptitude for logical reasoning, critical thinking, and complex problem-solving.
Bachelor's Degree in Computer Science, Data Engineering, Information Systems, or a related technical field.

Nice To Haves

Strategic mindset with the ability to define long-term data architecture vision, anticipate upstream and downstream challenges, and make data-informed decisions aligned with broader organizational objectives.
Resourceful, action-oriented innovator who consistently cuts through ambiguity and engineers creative solutions — particularly through the application of AI-driven technologies, intelligent automation, and emerging data tooling.
Experience leveraging AI-enabled tools and workflows within data engineering contexts — including familiarity with LLMs, RAG pipelines, and intelligent automation — with a demonstrated ability to apply these technologies to meaningfully improve speed, quality, and operational output.
Deep familiarity with open table formats (Apache Iceberg, Delta Lake).
Deep familiarity with cloud-native data systems.
Deep familiarity with self-service data platform principles including data mesh architectures.
Familiarity with graph databases and SPARQL as a forward-looking capability.
Demonstrated expertise implementing data privacy frameworks — including hands-on experience building systems compliant with global regulations (e.g., GDPR, CCPA).
Proficiency with data visualization and reporting tools such as Tableau, Superset, or equivalent platforms — with an ability to translate complex data into clear, consumable insights for business audiences.
Familiarity with the structural nuances of diverse digital media catalogs — audio, video, and publishing data models.
A genuine passion for Apple products and services, with deep familiarity with the Apple ecosystem and an understanding of the content and media landscape that drives Apple Music and beyond.

Responsibilities

Architect and deploy robust, AI-ready data pipelines.
Build extensible, modular systems spanning across Music, Video, and Books.
Manage complex content taxonomies, variable provider data, and hundreds of analytical dimensions.
Deliver accurate, actionable insights to cross-functional teams, partner organizations, and executive leadership.
Own production data systems at massive scale.
Design and operate CI/CD pipelines, automated deployment workflows, and version-controlled data infrastructure.
Implement programmatic anomaly detection on high-volume datasets.
Lead projects, influence cross-functional teams, and drive consensus in a matrixed organization.
Translate stakeholder needs into scalable, well-scoped technical solutions.
Articulate complex technical concepts to non-technical audiences.
Effectively influence stakeholders at all levels.
Mentor peers and champion a culture of data engineering excellence across the team.