At Apple, we find joy in solving hard problems, and we are motivated by curiosity to keep making our products better. Apple Services Engineering's Device Compute Analytics team provides the data foundation for Apple's internal device cloud, which supports all of Apple's hardware, software, and services. Our work helps Apple ship entirely new products and features, and gives teams the data to keep existing ones meeting a high standard.
We are looking for a senior data engineer to own our data lakehouse: turning a stream of device and compute events into data that is accurate, timely, and easy to explore. You will build the pipelines and table designs that power forecasting, capacity planning, and fair cross-charging across the organization, and help us spot issues before an internal customer ever notices them. Join a high-performing team, work with colleagues across Apple, and make data that people across the company rely on.
Description
You will own the data lakehouse that turns raw device and compute events into reliable, fast-to-query tables. You will build and run the Spark pipelines, design the tables that dashboards query through Trino, and put in place the checks that keep the data accurate and timely. Over time, you will also take over the ingestion of those events from Kafka.
Our services publish events describing device lifecycle, job and session activity, provisioning, and failures, and those events land in Iceberg. You will turn them into datasets people can trust and build on, working with a data analyst and a full-stack engineer (who builds the dashboards) on the team, as well as application engineers and teams across Apple.
Minimum 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 (Structured Streaming) pipelines
Strong data modeling skills, with experience 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 the discipline to validate generated code and queries
Excellent communication and critical thinking skills in a team environment
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
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: 90733111
- Position Id: 2103d4aa76d2d5bfadd7a62b1165f34b
- Posted 2 days ago
