Senior Data Engineer

Apple Services Engineering is building device compute analytics that help teams move faster with accurate, timely, fast-to-query data. In the Device Compute Analytics team, you’ll own a data lakehouse that transforms raw device and compute events into trusted tables used for analytics and dashboards.

This is an onsite role in Cupertino, CA offering an annual salary range of USD 184,700 - 324,800. The team works cross-functionally with data analysts, full-stack engineers, application engineers, and other groups across Apple.

What you’ll work on

• Own the data lakehouse that converts raw device and compute events into reliable, fast-to-query tables.
• Build and operate Spark pipelines, including the ingestion and transformation logic behind datasets used downstream.
• Design analytics tables that are queried via Trino, with a focus on structure that supports performance and usability.
• Implement data quality checks to keep the data accurate and timely, including safeguards that help maintain freshness and completeness.
• Over time, take over ingestion from Kafka to bring events into the lakehouse.
• Turn event streams related to device lifecycle, job and session activity, provisioning, and failures into datasets people can trust.
• Collaborate with a team consisting of a data analyst and a full-stack engineer who builds the dashboards, as well as application engineers and partner teams across Apple.

Required qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• 7+ years of professional data engineering experience building production data pipelines at scale, including diagnosing and improving the performance of data jobs and queries, or equivalent advanced degree plus experience.
• Production experience with Apache Spark, including batch and streaming using Structured Streaming.
• Strong data modeling skills, including designing analytics schemas on Apache Iceberg (or comparable lakehouse table formats).
• Proficiency in Java or Scala, and strong SQL.
• Experience building data quality checks and monitoring for production pipelines, including freshness and completeness alerting and reconciliation between source and derived data.
• Productive use of AI coding tools in real delivery, with a discipline to validate generated code and queries.
• Excellent communication and critical thinking in a team environment.

Technology stack

Apache Spark, Structured Streaming, Apache Iceberg, Trino, Kafka, SQL, Java, Scala

Benefits

• Comprehensive medical and dental coverage
• Retirement benefits
• A range of discounted products and free services
• Reimbursement for certain educational expenses, including tuition
• Discretionary bonuses or commission payments as well as relocation (if eligible)
• Opportunity to become an Apple shareholder through Apple’s discretionary employee stock programs
• Eligibility for discretionary restricted stock unit awards
• Ability to purchase Apple stock at a discount if voluntarily participating in the Employee Stock Purchase Plan

Preferred qualifications

• Production experience with Apache Kafka as a streaming source
• Production experience with Apache Flink, including the Iceberg sink, exactly-once delivery, and stateful jobs on Kubernetes
• Experience with data contracts and schema management as code (for example schema registries or dbt model contracts)
• Experience tuning tables and queries for a distributed SQL engine such as Trino
• Experience with workflow orchestration and Python automation
• Experience building dashboards or data applications that put data in front of users, with attention to visual design
• Experience owning the design of a data platform or pipeline, not just implementing it
• Experience mentoring engineers and leading cost or reliability improvements