Summary
The people here at Apple don't just create products - they create the kind of wonder that's revolutionized entire industries. It's the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it. We work in a startup atmosphere where individuals take ownership and have significant impact on the final product. We are a dynamic team within Apple’s Worldwide Sales organization, Data Solutions \& Initiatives—focused on driving innovation through product design, engineering, and portfolio management. In our startup-like environment, we move quickly, experiment boldly, and expect our team to take full ownership of what they deliver.
Description
You’ll design and build core components of our internal data platform, spanning ingestion pipelines, semantic layers, and metadata systems as we move towards a data mesh. You’ll help bridge open source technologies (e.g., Kafka, Spark, Iceberg) with our internal ecosystem—shaping how teams discover, use, and trust data for analytics and AI workloads.
You’ll collaborate with other engineers, product managers, program managers, and end users to understand data needs and evolve platform capabilities that improve scale, quality, and usability. This is a hands-on engineering role focused on systems thinking, technical craftsmanship, and delivering tools that unlock real business value.
Responsibilities
Build scalable, cloud-native data systems that support data exploration, reporting, and production ML/AI use cases
Integrate open-source components with internal tools and APIs to streamline platform usability
Develop and maintain data ingestion pipelines, metadata services, and performance-optimized storage layers
Ensure the platform supports AI-readiness, including high-quality, discoverable, and semantically rich data
Collaborate with internal customers to understand workflows and shape new platform features
Partner with engineers, EPMs, and US-based teams to ensure alignment, reusability, and shared standards
Support production systems through monitoring, debugging, and operational improvements
Minimum Qualifications
7+ years of experience building distributed data applications and cloud-native platforms
AI/ML pipeline enablement (building real-time pipelines, feature stores, vector databases)
Exposure to GenAI use cases and their data and pipeline requirements
Proficiency in Python, Scala/Java, with experience developing scalable and maintainable systems
Strong SQL skills and experience with cloud data warehouses (e.g., Snowflake, BigQuery)
Experience with modern data infrastructure tools (e.g., Spark, Kafka, Airflow, Iceberg)
Experience with Kubernetes, distributed compute frameworks, or containerized environments
Ability to build CI/CD pipelines to support large scale AI systems
Preferred Qualifications
Commitment to data engineering best practices including version control, automated testing, data quality checks.
Experience designing and building cloud-based applications, APIs, and data services
Understanding of BI and analytics needs, and experience building for internal business use cases
Hands-on experience integrating with business intelligence or visualization tools (e.g., Streamlit, Tableau, Looker)
Experience working in global teams or serving as a technical contributor in a regional hub
Apple is an equal opportunity employer that is committed to inclusion and diversity. Apple provides reasonable accommodations to applicants with disabilities and in accordance with local requirements. Apple is a drug-free workplace.