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Data Engineer, Data Solutions & Initiatives

Apple Oakbrook

Data Engineer, Data Solutions & Initiatives Austin, Texas, United States Sales and Business Development Imagine what you could do here. 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.Data Solutions & Initiatives (DSI) is a dynamic team within Apple’s Worldwide Sales organization. Our mission is to drive innovation through product design, engineering, and portfolio management. We thrive in a startup-like atmosphere where individuals take ownership and have a significant impact on the final product. In this environment, we move quickly, experiment boldly, and expect our team to take full ownership of what they deliver. Description As a DSI Data Engineer, you will 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. Your role involves bridging 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 will 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 hands-on engineering role emphasizes 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, engineering program managers, 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 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. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant . Apple accepts applications to this posting on an ongoing basis. #J-18808-Ljbffr

Vacancy posted 21 hours ago
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