Job description

At Apple Ads, we are building the next generation of privacy-focused advertising capabilities. As part of the data organization, we work at the cutting edge of data engineering, machine learning, and privacy at Apple scale. We are constantly developing data products to provide amazing user experiences and to drive value for developers and publishers. As a member of the Data Products Engineering team, you will: - Engineer and operate secure, scalable data processing systems across real-time, near-real-time, and batch execution contexts using Spark, Kafka, and Iceberg - Deliver features, schema changes, backfills, and fixes with clean, well-tested, well-documented code, and help support privacy-preserving critical pipelines - Depending on level, own the design of pipeline components, define and refactor approaches to meet correctness and scale challenges, and drive reliability and efficiency improvements - Use AI coding agents to accelerate implementation, test coverage, and debugging, validating every result for correctness, privacy, and cost - Apply a strong understanding of the intersection between business, analytics, and engineering, taking a proactive approach focused on reusable solutions to improve efficiency and time to insight - Contribute to on-call, monitoring, and the continuous improvement of pipeline reliability and efficiency; more senior engineers help lead incident response and root-cause analysis - Collaborate with a team of world-class data engineers and product managers; grow through code and design reviews, and mentor other engineers as you gain seniority - Work effectively in a rapidly changing, sprint-based, agile environment, and contribute to a culture that emphasizes reliability, resiliency, extensibility, scalability, and productivity. We are one team, nurturing each other's growth and supporting each other in delivering for our customers and Apple 5+ years of industry experience building distributed, scalable data pipelines Proven track record of successful and timely software delivery with complex dependencies Demonstrated innovative, critical thinking and troubleshooting skills Proficiency with modern programming languages (for example Scala, Java, or Python) and ease with picking up new technologies independently Strong computer science and data-engineering fundamentals Proficiency in distributed data processing technologies (for example Spark, Kafka, Flink) and data modeling with Iceberg lakehouse concepts Experience building and scaling systems on premise and in the cloud Demonstrated productive use of AI coding tools in real delivery Passion for data quality, code elegance, clear documentation, cost saving, and operational excellence Excellent collaborative skills Experience owning the design and architecture of pipeline components or services, not just implementing them Experience with near-real-time or streaming pipelines, event-driven ingestion, deduplication, parity and reconciliation, and lakehouse table formats at scale History of driving reliability, efficiency, or cost improvements, and mentoring other engineers Contribution to open source distributed system projects Ability to communicate effectively with cross-functional technical and non-technical teams Comfortable working in a rapidly changing environment with ambiguous requirements Comfortable with rotating on-call for mission-critical applications Prior experience in the advertising industry is a huge plus

Requirements

Must-haves

Engineer and operate secure, scalable data processing systems across real-time, near-real-time, and batch execution contexts using Spark, Kafka, and Iceberg
Deliver features, schema changes, backfills, and fixes with clean, well-tested, well-documented code, and help support privacy-preserving critical pipelines
Depending on level, own the design of pipeline components, define and refactor approaches to meet correctness and scale challenges, and drive reliability and efficiency improvements
Use AI coding agents to accelerate implementation, test coverage, and debugging, validating every result for correctness, privacy, and cost
Apply a strong understanding of the intersection between business, analytics, and engineering, taking a proactive approach focused on reusable solutions to improve efficiency and time to insight
Contribute to on-call, monitoring, and the continuous improvement of pipeline reliability and efficiency; more senior engineers help lead incident response and root-cause analysis
Collaborate with a team of world-class data engineers and product managers; grow through code and design reviews, and mentor other engineers as you gain seniority
5+ years of industry experience building distributed, scalable data pipelines
Proven track record of successful and timely software delivery with complex dependencies
Demonstrated innovative, critical thinking and troubleshooting skills
Proficiency with modern programming languages (for example Scala, Java, or Python) and ease with picking up new technologies independently
Strong computer science and data-engineering fundamentals
Proficiency in distributed data processing technologies (for example Spark, Kafka, Flink) and data modeling with Iceberg lakehouse concepts
Experience building and scaling systems on premise and in the cloud
Demonstrated productive use of AI coding tools in real delivery
Passion for data quality, code elegance, clear documentation, cost saving, and operational excellence
Excellent collaborative skills
Experience owning the design and architecture of pipeline components or services, not just implementing them
Experience with near-real-time or streaming pipelines, event-driven ingestion, deduplication, parity and reconciliation, and lakehouse table formats at scale
History of driving reliability, efficiency, or cost improvements, and mentoring other engineers
Contribution to open source distributed system projects
Ability to communicate effectively with cross-functional technical and non-technical teams
Comfortable working in a rapidly changing environment with ambiguous requirements
Comfortable with rotating on-call for mission-critical applications

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaiagilejavapython

Platform: apple
Effort: medium