Senior Data Engineer, Data Products

At Apple, we focus deeply on our customers' experience. Apple Ads brings this same approach to advertising, helping people find exactly what they are looking for and helping advertisers grow their businesses. Our technology powers ads and sponsorships across Apple Services, including the App Store, Apple News, Apple Maps, MLS Season Pass, and F1. Everything we do is designed for trust, connection, and impact: we respect user privacy, integrate advertising thoughtfully into the experience, and deliver value for advertisers of all sizes, from small app developers to big global brands. Because when advertising is done right, it benefits everyone. The Data Products Engineering team is seeking versatile data engineers to build, extend, and operate the near-real-time and batch pipelines at the heart of Apple Ads, and to help design and improve them as you grow. You will ship features, schema changes, backfills, and improvements to petabyte-scale funnel and aggregate pipelines, including privacy-preserving critical pipelines, with strong ownership of quality, reliability, and time to delivery. Depending on your level, you will also own the design of pipeline components, drive reliability and efficiency improvements, and mentor other engineers. You will join a team of world-class data engineers with an appetite for applying leading-edge technologies, and collaborate closely with your pod and with the business to deliver relevant data and insight. This is a high-velocity, AI-native role: you will use AI agents daily to move fast without cutting corners on correctness. You should have 5 plus years of data engineering experience, ideally within the ads or media space, a strong understanding of scalable approaches, and the ability to thrive and stand out under tight deadline constraints in Agile environments. We will calibrate the level and scope to your experience.

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

Minimum Qualifications

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

Preferred Qualifications

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