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 Bachelors Degree in Computer Science, Data Engineering, Information Systems, or a related technical fieldStrategic 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. Build & Scale Data Infrastructure: Architect and deploy scalable, modular data pipelines optimized for Apple Musics massive scale - delivering reliable, self-service data products with clear contracts and SLAs that internal teams, data scientists, and external partners can confidently consume.