Senior Software Engineer

Do you enjoy crafting elegant solutions to distributed problems with billions of data points per day Can you manage business complexity and still deliver reliable scalable systems If so join us! You will work with engineers across Apple to build data pipelines and real-time streaming applications that power Apple Music Shazam Apple Podcasts and Fitness

We build big-data and real-time data platform components and pipelines. We are domain experts who work alongside product business and client engineering teams. AI is already a core part of how we work. We have access to leading frontier tools and use agentic engineering to accelerate our development operations and support work. Our data supports a modern AI stack powering features and recommendations.

Build at scale: Design build and evolve distributed compute frameworks data pipelines and real-time streaming applications that process billions of data points per questions into specs: Work with partners to understand what they need and why. Write clear technical specs that define datasets metrics and requirements and review specs written by with partners: Work on cross-functional projects with engineering teams product leads data scientists and analytics leaders to build datasets metrics and technical direction: Lead initiatives from problem discovery through to production. Weigh cost complexity and performance and choose the right tool for each it well: Share ownership of high-throughput low-latency production systems. That includes on-call and hardening systems against failure before it the bar: Do thorough code and design reviews mentor other engineers and champion testing and engineering best practices.

Substantial experience designing building and operating business-critical large-scale distributed in at least one statically typed language ideally Java or Scala and a strong grasp of big-data fundamentals: concurrency memory management networking and performance -depth understanding of at least one distributed or big-data system such as Kafka Flink Spark Trino Iceberg or gathering requirements and turning ambiguous business needs into clear accurate technical focus on quality and craftsmanship: You reason well about complex failure modes and deliver well-tested maintainable code.

Practical experience maintaining large-scale data pipelines with Spark and with cloud container and infrastructure tools such as Kubernetes and use of AI tools such as Claude in day-to-day to open-source data-processing written and verbal communication across distributed or Masters degree in Computer Science Software Engineering or equivalent experience.