Highlights
Build and run Spark streaming and batch pipelines in Scala or Java, including backfills when data issues are found or new transformations are added for past events Design lakehouse tables and layouts (partitioning, sort order, compaction, schema evolution) so dashboards can query Iceberg quickly through Trino Validate incoming events, handle schema changes, and route malformed or unknown events to a dead-letter path for review and replay Define and enforce data quality with a data analyst: keep schemas and data contracts under version control with compatibility checks in CI, monitor freshness, completeness, duplicates, and late arrivals, and alert the right owner Partner with the full-stack engineer who builds the dashboards, designing the tables and queries behind them and using AI coding tools to move faster while checking the results Take over the Apache Flink job that ingests events from Kafka into Iceberg Improve performance and cost by right-sizing jobs, fixing skew and small-file problems, and tuning queries and storage Join the on-call rotation for Data Lakehouse incidents, review code and designs, and mentor other engineersBachelors 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 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. 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.
Numbers & Facts
| Location | Cupertino, CA |
| Industry | Computer/IT Services |
| Company Size | 10,000 employees or more |
| Year Founded | 1976 |
| Website | https://www.apple.com/jobs |
Description
At Apple, we find joy in solving hard problems, and we are motivated by curiosity to keep making our products better. Apple Services Engineerings Device Compute Analytics team provides the data foundation for Apples internal device cloud, which supports all of Apples 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.Build and run Spark streaming and batch pipelines in Scala or Java, including backfills when data issues are found or new transformations are added for past events Design lakehouse tables and layouts (partitioning, sort order, compaction, schema evolution) so dashboards can query Iceberg quickly through Trino Validate incoming events, handle schema changes, and route malformed or unknown events to a dead-letter path for review and replay Define and enforce data quality with a data analyst: keep schemas and data contracts under version control with compatibility checks in CI, monitor freshness, completeness, duplicates, and late arrivals, and alert the right owner Partner with the full-stack engineer who builds the dashboards, designing the tables and queries behind them and using AI coding tools to move faster while checking the results Take over the Apache Flink job that ingests events from Kafka into Iceberg Improve performance and cost by right-sizing jobs, fixing skew and small-file problems, and tuning queries and storage Join the on-call rotation for Data Lakehouse incidents, review code and designs, and mentor other engineersBachelors 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 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
About Company
We bring amazing people together to make amazing things happen.
We’re a diverse collection of thinkers and doers, continually reimagining what’s possible to help us all do what we love in new ways. The people who work here have reinvented entire industries with the Mac, iPhone, iPad, and Apple Watch, as well as with services, including iTunes, the App Store, Apple Music, and Apple Pay. And the same passion for innovation that goes into our products also applies to our practices — strengthening our commitment to leave the world better than we found it.
About Apple
There’s a place here for every kind of brilliant. Everyone here is an innovator, or an innovator-to-be, no matter what your team or your role. So bring your passion, courage, and original thinking and get ready to share it, because every new product, service, or feature we invent is the result of people working together to make each others’ ideas stronger. Innovation at this level depends on people who represent the variety of the human experience and inspire us with their own fresh perspectives. Together, we’ll do amazing work that can make a difference in people’s lives. Including your own. Learn more about working at Apple.