Senior Data Engineer — Music Services… at Apple · Location…

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. Build & Scale Data Infrastructure: Architect and deploy scalable, modular data pipelines optimized for Apple Music's massive scale — delivering reliable, self-service data products with clear contracts and SLAs that internal teams, data scientists, and external partners can confidently consume. Champion AI & Innovation: Proactively identify and integrate AI-enabled tools, intelligent automation, and advanced analytics workflows into existing data systems. Actively explore how LLMs, RAG pipelines, and emerging technologies can elevate team capabilities and drive operational efficiency. Champion best practices for AI-augmented data engineering across the organization. Drive Data Quality & Observability: Build robust validation frameworks to sanitize unpredictable third-party and provider feeds. Implement automated data assertions, lineage tracking, advanced anomaly detection, and monitoring to guarantee full transparency and reliability across the production data lifecycle. Enable Advanced Analytics: Partner closely with data scientists, analysts, and cross-functional stakeholders to deliver structured, high-performance datasets backed by clear SLAs — accelerating insights and systematically reducing dependency on ad-hoc engineering requests. Cross-Functional Partnership: Collaborate and communicate effectively across Analytics, Operations, Engineering, and Partner teams. Act as a proactive technical partner to the business — translating ambiguous requirements into resilient engineering solutions that align with organizational priorities. Operational Excellence & Self-Service: Lead the team's evolution toward scalable, self-service analytics. Enforce strict CI/CD practices, mentor peers, and foster a strong data engineering culture centered on quality, reliability, and continuous improvement. 7+ years of professional experience in data engineering or systems architecture, with a 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, with expertise in distributed data processing frameworks (e.g., PySpark/Spark), modular software design, and 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, resolving complex taxonomy mapping issues, and implementing programmatic anomaly detection on high-volume datasets Proven ability to lead projects, influence cross-functional teams, and drive consensus in a matrixed organization — translating stakeholder needs into scalable, well-scoped technical solutions Exceptional written and verbal communication skills, with the ability to articulate complex technical concepts to non-technical audiences and effectively influence stakeholders at all levels 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 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 A 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), cloud-native data systems, and 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 Experience mentoring peers and championing a culture of data engineering excellence across the team 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