Service Reliability Engineer- Apple Data Platforms

The Apple Services Engineering team (ASE) is one of the most exciting examples of Apple's long-held passion for combining art and technology. These are the people who power the App Store, Apple TV, Apple Music, Apple Podcasts, and Apple Books — at extensive scale, meeting high expectations to deliver a huge variety of entertainment in over 35 languages to more than 150 countries.

Within ASE, the Apple Data Platform SRE team keeps a massive, multi-cloud platform running for thousands of internal engineers building the next generation of data and AI products at Apple. We sit at the intersection of infrastructure, automation, and customer success — running incident response, providing hands-on support to internal teams, and partnering with developers to make cutting-edge services like Spark, Flink, Airflow, Trino, Druid, Notebooks, and LLM-based agent platforms reliable at scale.

Description

This is a rare opportunity to build deep expertise across one of the most technically diverse platforms at Apple — while specialising in the big data engines and catalog/governance layers that power analytics and data engineering across the company.

As an SRE on Apple Data Platform, you'll operate and support the team's full portfolio, from ML/AI platform services to multi-cloud infrastructure, and grow into the team's go-to expert for big data platform services — including Spark, Flink, Airflow, Trino, Notebooks, REST Catalog services (such as Glue Catalog), and data governance. Just as importantly, you'll be a first point of contact for the internal customers who rely on these services daily — someone who can translate a confusing error or a vague support request into a clear diagnosis and a fast resolution.

We're looking for a self-motivated engineer who thrives on ownership — someone who wants a set of services to call their own, the autonomy to drive their reliability roadmap, and the collaborative instinct to keep that work aligned with the team's broader direction. If you love solving hard operational problems, take genuine satisfaction in helping frustrated customers get unblocked, and want a front-row seat to how Apple's data engineering platform scales, this role offers real room to grow your scope and impact over time.

Minimum Qualifications

Bachelor's Degree in Computer Science, an engineering-related field, or equivalent related experience. 7+ years in a Site Reliability Engineering, DevOps, or Infrastructure-focused role. Proficient in scripting languages (Shell, Python) Deep understanding of one or more Big Data technologies (Spark, Flink, Airflow, Trino, Notebooks). Strong Experience with Kubernetes and AWS services- handling IAM (roles, policies, permission boundaries, KMS), EKS, RDS, S3 and cloud storage/networking fundamentals. Strong experience with implementing observability systems and practices: metrics, logging, tracing, alerting, SLOs, error budgets, and fault analysis at scale with tools like Prometheus, Grafana, Splunk, PagerDuty. Excellent written and verbal communication skills, with the ability to explain technical issues clearly to non-expert customers. Solid grounding in SRE principles, with prior on-call, production-support, or customer-facing support role experience.

Preferred Qualifications

Familiarity with MCP-based tooling and ML lifecycle/dataset management systems. Prior experience in a customer-facing or technical support role, with a demonstrated passion for customer success. Working knowledge of CI/CD pipelines and deployment workflows. Familiarity with data pipeline orchestration and workflow scheduling patterns. Proficient in handling production databases and distributed-SQL engines with experience in Query Tuning and Optimizations. A track record of automating manual operations through scripting or tooling. Intellectual curiosity and a drive to keep learning — for yourself, your team, and the org. Comfortable leveraging AI-assisted development tools (e.g., Claude Code) to accelerate prototyping, code generation, and documentation, and able to critically review and validate AI-generated output before it ships.