You’ll operate and harden our big data platform — built on open source and other technologies — that powers critical applications like analytics, reporting, and AI/ML. This means driving down MTTR, automating operational toil, tuning performance and cost, running capacity planning, and root-causing production incidents before and after they happen. You’re an independent, self-directed problem-solver who communicates clearly with both engineers and non-technical partners, and you’ll work across many teams to keep the platform running at Apple’s standard.
Minimum Qualifications
5+ years of experience operating and supporting critical, large-scale distributed systems in production, with scripting/programming ability in Python, Go, Java, Scala, or Bash for automation and tooling.
Deep understanding of reliability principles — fault tolerance, high availability, low latency, graceful degradation — and how to instrument and enforce them (SLIs/SLOs, alerting, on-call practices, incident response, postmortems).
Hands-on experience operating data processing ecosystems and distributed computing frameworks (Spark, Flink) and MPP query engines (Trino, StarRocks), including performance tuning and capacity management.
Proficiency operating Kubernetes/Helm at scale, building and maintaining CI/CD pipelines (GitHub Actions, Jenkins), managing infrastructure as code (Terraform, Pulumi), and running service-oriented architectures across multi-cloud environments.
Strong troubleshooting and performance analysis skills in complex production environments; fluency in Unix/Linux and command-line diagnostics, with excellent problem-solving and communication skills.
Preferred Qualifications
Experience contributing to open source projects or operating across multiple public cloud providers.
In-depth operational knowledge of specific distributed frameworks — Spark, Flink, or Kafka Streams, Trino, Iceberg — including debugging via component-specific logs, and experience managing multi-tenant Kubernetes clusters at scale.
Experience with workflow/pipeline orchestration tools (Airflow, dbt) and understanding of data modeling and warehousing concepts.
Experience debugging Kubernetes/Spark production issues via logs and metrics, with a continuous-improvement mindset for self, team, and org.
Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.