Sr Software Engineer

Apple • Cupertino, CA

21h

About The Position

Apple Services Engineering's Device Compute Analytics team provides the data foundation for Apple's internal device cloud, supporting Apple's hardware, software, and services. The role owns the team's data lakehouse, transforming device and compute events into accurate, timely, reliable, and easily explored data. The engineer builds and runs Spark pipelines, designs Iceberg tables queried through Trino, and implements data checks to ensure accuracy and timeliness. The role will also take over event ingestion from Kafka over time. Event data includes device lifecycle, job and session activity, provisioning, and failures. The resulting datasets support forecasting, capacity planning, fair cross-charging, dashboards, and proactive issue detection. The engineer collaborates with a data analyst, a full-stack engineer, application engineers, and teams across Apple.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field, plus 7+ years of professional data engineering experience building production data pipelines at scale, including diagnosing and improving data job and query performance; or an equivalent advanced degree plus experience.
• Production experience with Apache Spark, including batch and streaming (Structured Streaming) pipelines.
• Strong data modeling skills and experience designing analytics schemas on Apache Iceberg or comparable lakehouse table formats.
• Proficiency in Java or Scala and strong SQL skills.
• 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 discipline to validate generated code and queries.
• Excellent communication and critical thinking skills in a team environment.

Nice To Haves

• 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, such as 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.

Responsibilities

• Own the data lakehouse that transforms raw device and compute events into reliable, fast-to-query tables.
• Build and operate Spark pipelines, including batch and streaming pipelines.
• Design tables for dashboards queried through Trino.
• Implement checks and monitoring to ensure data accuracy and timeliness.
• Take over ingestion of events from Kafka over time.
• Create trusted datasets from device lifecycle, job and session activity, provisioning, and failure events.
• Work with a data analyst, a full-stack engineer, application engineers, and teams across Apple.
• Build data pipelines and table designs that support forecasting, capacity planning, and fair cross-charging.
• Help detect issues before internal customers notice them.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior