1 month ago
Responsibilities
- Engineer and operate secure, scalable data processing systems across real-time, near-real-time, and batch execution contexts.
- Deliver features, schema changes, backfills, fixes, and privacy-preserving improvements for petabyte-scale data pipelines.
- Design and refactor pipeline components to address correctness, scalability, reliability, and efficiency challenges.
- Use AI coding agents to accelerate implementation, testing, and debugging while validating correctness, privacy, and cost.
- Improve reusable solutions, data quality, operational excellence, time to insight, and cost efficiency.
- Contribute to on-call rotations, monitoring, incident response, root-cause analysis, and continuous pipeline reliability improvements.
- Collaborate with engineers, product managers, and cross-functional stakeholders through code and design reviews.
- Mentor other engineers as seniority and scope increase.
Requirements
- 5+ years of industry experience building distributed, scalable data pipelines.
- Proven success delivering software on time in systems with complex dependencies.
- Strong critical-thinking, troubleshooting, computer science, and data-engineering fundamentals.
- Proficiency in a modern programming language such as Scala, Java, or Python and ability to learn new technologies independently.
- Proficiency with distributed data processing technologies such as Spark, Kafka, or Flink and data modeling with Iceberg lakehouse concepts.
- Experience building and scaling systems on premises and in the cloud.
- Demonstrated productive use of AI coding tools in real software delivery.
- Experience or interest in data quality, code quality, documentation, cost savings, and operational excellence.
- Excellent collaboration and communication skills.
- Preferred experience owning pipeline component or service design and architecture.
- Preferred experience with streaming pipelines, event-driven ingestion, deduplication, parity and reconciliation, and lakehouse table formats at scale.
- Preferred history of driving reliability, efficiency, or cost improvements and mentoring engineers.
- Open source distributed-systems contributions are preferred.
- Comfort with ambiguous requirements, rapidly changing environments, and rotating on-call for mission-critical applications.
- Prior advertising-industry experience is a plus.
Tech Stack
Categories
Data Engineering
About Apple
Apple designs and sells consumer electronics, software, and services for consumers and professionals worldwide, including iPhone, Mac, iPad, Apple Watch, and AirPods, plus platforms like iOS/macOS and services such as the App Store, iCloud, Music, and TV+. Its business combines device sales with services and subscriptions and in-house silicon design. Founded in 1976, Apple is headquartered in Cupertino, California, and trades on NASDAQ as AAPL.