Software Engineer - Data Solutions

Apple’s Data Solutions team, within the broader ASE Data Services organization, builds and operates data infrastructure that is reliable, scalable, and low-latency. The team focuses on real-time data streaming, large-scale data processing, and optimizing cross-datacenter replication to bring user data to the edge with the smallest possible latency. Apache Kafka plays a central role in our streaming and replication infrastructure, and familiarity with its ecosystem is a meaningful advantage. Our systems sit at the heart of services like Apple Music, TV, and Podcasts, ensuring that data is available where and when it’s needed - anywhere in the world. A key focus for this team is automating existing systems and integrating them into a centralized cloud platform - modernizing the infrastructure that underpins these services and building the tooling that lets us operate them at scale. Engineers on this team own their platforms end-to-end: from internals and protocol-level work to operational tooling, observability, and multi-region deployment.As a member of this team, you will build and evolve foundational components of Apple’s data replication platform. Areas of work include:Real-time data streaming and event-driven pipelinesCross-datacenter replication and consistencyEdge delivery optimization for lowest-latency data accessPlatform reliability, observability, and incident responseAutomation of existing systems and integration into centralized cloud platforms

• Proficiency in Java or equivalent, with strong understanding of concurrency, memory management, and performance.
• Experience designing, building, and operating large-scale distributed systems.
• Solid understanding of data structures, algorithms, fault tolerance, and system performance.
• Experience with RESTful API design and service-oriented architectures.
• Bachelor’s degree in Computer Science or equivalent practical experience.

Preferred Qualifications

• Experience with distributed data systems such as Cassandra, Redis, Kafka, or similar platforms.
• Experience with Apache Kafka - including broker internals, producers/consumers, and ecosystem tooling - is a strong plus.
• Experience with multi-datacenter deployments, replication strategies, and consistency models.
• Hands-on experience with cloud platforms and container orchestration (e.g. Kubernetes, AWS, GCP, or similar).
• Exposure to observability practices including monitoring, alerting, and performance benchmarking.
• Experience with fault injection, chaos engineering, or property-based testing methodologies.
• Contributions to open-source projects, especially in the data infrastructure ecosystem.

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