Data Site Reliability Engineer, AWS & Big Data

Apple Inc.; Austin (TX); On-site; USD 140,000 - 190,000; Full time

Job summary

Apple Inc. is hiring a Data Site Reliability Engineer for Apple Ads in Austin, TX. You will design, build and operate cloud-based data systems, ensuring reliability, security, and observability across AWS services and multi-cloud environments.

You will collaborate with advertising, ML, and data platforms teams to drive production excellence, automate toil, and enable scalable deployments at scale. Strong Kubernetes and Open Source data processing expertise are essential.

Qualifications

• 2+ years of infrastructure engineering with a focus on cloud-based distributed data systems.
• Proven experience running cloud infrastructure built on AWS managed services.
• Experience with Open Source data processing tech like Spark, Flink, Kafka, Iceberg or similar.
• Strong expertise with container orchestration systems like Kubernetes.
• Experience leveraging ML and GenAI to improve infrastructure and operations.

Responsibilities

• Design, build and manage large-scale distributed data systems on AWS (EMR, EKS, MSK, Iceberg).
• Develop tooling and automation to improve reliability, cost-efficiency and observability.
• Collaborate with cross-functional teams to define infrastructure architecture and troubleshoot issues.
• Lead automation initiatives to reduce toil and enable scalable deployments.
• Contribute to multi-cloud strategy to sustain excellence while enabling future innovation.

Skills

Cloud infrastructure design

Open Source data processing

GenAI for operations

Kubernetes

Java/Scala/Python programming

Tools

AWS

EKS

MSK

Iceberg

Spark

Flink

Kafka

Helm

CRD

Job description

Apple Inc. is hiring a Data Site Reliability Engineer for Apple Ads in Austin, TX. You will design, build and operate cloud-based data systems, ensuring reliability, security, and observability across AWS services and multi-cloud environments.

You will collaborate with advertising, ML, and data platforms teams to drive production excellence, automate toil, and enable scalable deployments at scale. Strong Kubernetes and Open Source data processing expertise are essential.