Senior Data Reliability Engineer — Cloud-scale on AWS & Kubernetes

Apple Inc.; Austin (TX); On-site; USD 140,000 - 210,000; Full time

Job summary

Apple Inc. in Austin, Texas is seeking a Senior Data Site Reliability Engineer for Apple Ads.

You will design, build, and operate cloud-based data infrastructure that stores, processes, and analyzes petabyte-scale datasets, collaborating with data science and product teams to ensure reliability and performance. The role emphasizes strong experience with AWS, Kubernetes, Spark, Flink, Kafka and Iceberg, plus a focus on automation, cost optimization, and production excellence across distributed

Qualifications

• 5+ years of experience in infrastructure engineering focused on cloud data systems.
• Proven experience with AWS cloud services and managed infrastructure.
• Experience with Spark, Flink, Kafka, Iceberg or similar data technologies.
• Experience with Kubernetes.
• Experience leveraging ML and GenAI capabilities to improve infrastructure and operational efficiency.

Responsibilities

• Design, build and manage large-scale distributed data systems on AWS.
• Develop tooling to improve data infrastructure reliability, cost-efficiency, and observability.
• Collaborate with cross-functional engineering teams to define infrastructure architecture and troubleshoot issues.
• Lead automation initiatives to reduce toil and improve deployment patterns.
• Contribute to multi-cloud strategy while maintaining current stack reliability.

Skills

Cloud computing

Reliability engineering

Data platforms

Incident response

Cost optimization

Tools

AWS

Kubernetes

Apache Spark

Kafka

Iceberg

Flink

Job description

Apple Inc. in Austin, Texas is seeking a Senior Data Site Reliability Engineer for Apple Ads.

You will design, build, and operate cloud-based data infrastructure that stores, processes, and analyzes petabyte-scale datasets, collaborating with data science and product teams to ensure reliability and performance. The role emphasizes strong experience with AWS, Kubernetes, Spark, Flink, Kafka and Iceberg, plus a focus on automation, cost optimization, and production excellence across distributed