Sr Software Engineer

Apple; Cupertino (CA); On-site; USD 150,000 - 220,000; Full time

Job summary

Apple is seeking a Senior Data Engineer to own the data lakehouse for the Device Compute Analytics team within Apple Services Engineering. You will build Spark pipelines, design lakehouse tables, and implement data quality checks to ensure accurate, timely data used across forecasting and capacity planning.

You will also take over ingestion of events from Kafka and collaborate with a data analyst and a full-stack engineer who builds dashboards, working with application engineers across Apple.

Qualifications

• Bachelor's degree required in CS/Engineering or related field.
• 7+ years data engineering experience building production pipelines.
• Proficiency with Spark (batch and streaming) and Iceberg or similar lakehouse formats.
• Strong data modeling, SQL, and programming in Java or Scala.

Responsibilities

• Own the data lakehouse for Apple Services Engineering's Device Compute Analytics.
• Build Spark pipelines and table designs powering dashboards via Trino.
• Implement data quality checks, monitoring, and freshness/reconciliation.
• Ingest events from Kafka and maintain reliable, timely datasets across teams.

Skills

Data engineering

SQL

Java/Scala

Production pipelines

Data modeling

Communication

Education

Bachelor's degree in Computer Science, Engineering, or a related field

Tools

Apache Spark

Apache Iceberg

Trino

Kafka

Flink

Kubernetes

dbt

Python/Java/Scala

Job description

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.

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