Senior Reliability Engineer - Data, Apple Ads

Apple•Austin, TX

About The Position

Apple Ads is seeking a Data Site Reliability Engineer to design, build, and manage cloud-based systems and infrastructure for storing, processing, transforming, and analyzing large datasets. The role focuses on keeping data and its supporting infrastructure accessible, reliable, and secure in cloud environments, including services from Apple’s internal and third-party cloud providers such as AWS. The engineer will build and operate large-scale distributed data systems on AWS, develop internal tooling and automation to improve reliability, cost efficiency, and observability, and work with advertising, machine learning, and data platform teams on infrastructure architecture and production excellence. The role also includes reducing operational toil, improving deployment patterns, and contributing to strategic decisions about multi-cloud adoption while maintaining operational excellence and enabling future innovation.

Requirements

At least 5 years of experience in infrastructure engineering, focused on building, scaling, and operating cloud-based distributed data systems.
Proven expertise running cloud infrastructure built on managed cloud-provider services such as AWS.
Strong technical knowledge and experience with open-source large-scale data processing technologies such as Apache Spark, Flink, Kafka, or Iceberg, or similar technologies.
Strong expertise and experience with container orchestration systems such as Kubernetes.
Experience using machine learning and generative AI capabilities to improve infrastructure and operational efficiency.
Track record of owning reliability and performance of cloud-native data platforms, including AWS and Kubernetes, with experience in capacity planning, cost optimization, and incident response for production-critical systems.
Experience designing, building, and operating large-scale distributed data infrastructure supporting petabyte-scale batch and streaming pipelines with high availability and low latency.
Experience partnering with data science, machine learning, and product teams to define infrastructure standards, automate deployment and observability tooling, and mentor engineers on distributed systems practices.
Track record of driving automation, cost optimization, and performance tuning at scale for data systems.
Experience designing, analyzing, and troubleshooting large-scale distributed systems.
Experience driving technology adoption and influencing system design.
Strong programming skills in object-oriented, functional, or procedural languages; Java, Scala, Kotlin, and Python are preferred.
Experience designing and managing Infrastructure as Code with Helm and CRDs.

Responsibilities

Design, build, and manage large-scale distributed data systems on AWS using services and technologies such as EMR, EKS, MSK, and Iceberg.
Design and develop internal tools and automation frameworks to improve data infrastructure reliability, cost efficiency, and observability.
Collaborate with advertising, machine learning, and data platform engineering teams to define infrastructure architecture, troubleshoot complex issues, and drive production excellence.
Lead initiatives to eliminate operational toil through automation and improve efficiency and scalable deployment patterns.
Contribute to strategic decisions about adopting a multi-cloud ecosystem while maintaining operational excellence on the current stack and enabling future innovation.
Own reliability and performance of cloud-native data platforms, including capacity planning, cost optimization, and production incident response.
Design, build, and operate large-scale distributed data infrastructure supporting petabyte-scale batch and streaming pipelines with high availability and low latency.
Partner with data science, machine learning, and product teams to define infrastructure standards and automate deployment and observability tooling.
Mentor engineers on distributed systems best practices.
Drive automation, cost optimization, and performance tuning for data systems at scale.
Design, analyze, and troubleshoot large-scale distributed systems.
Drive adoption of new technologies and influence system design.
Design and manage Infrastructure as Code with Helm and CRDs to support repeatable, secure, and scalable deployments.