You will operate and improve services within a very large-scale, highly-available Big Data ecosystem supporting Exabytes level of data with sustained, rapid growth. Your work directly enables the engineering and operations teams that build every Apple product. Own the reliability, performance, and scalability of services and infrastructure within the Insight ecosystem — with a build-to-manage mindset applied to design, deployment, and ongoing operations Build and advance AIOps capabilities across the ecosystem — developing ML-driven alerting, anomaly detection, LLM-assisted operational tooling, and automated incident triage that measurably improve observability and response Instrument services for deep observability: dashboards, meaningful alerts, runbooks, and clearly defined SLOs and SLIs that give the team and stakeholders an accurate view of ecosystem health Drive automation that reduces operational toil and improves ecosystem stability — key measures of success include ecosystem uptime, release quality, toil reduction, and sustained advancement of SRE practice Partner with incident management to drive effective incident response and conduct thorough post-incident reviews; translate findings into architectural and operational improvements that prevent recurrence Contribute to infrastructure-as-code practices and platform architecture decisions that shape the long-term direction of the Insight ecosystem Collaborate with cross-functional engineering teams across Apple's global manufacturing services, communicating effectively across time zones and cultures Help define the culture, working practices, and technical standards of the new EMEA operational team — build strong in-region relationships with business partners and represent the team's perspective within the wider Insight organization Mentor and support team members as the Cork operational team grows

Minimum Qualifications

Experience in SRE, DevOps, or platform engineering with strong communication skills and ability to collaborate effectively across geographies
Linux systems proficiency — comfortable troubleshooting at the systems level including processes, networking, file systems, and performance diagnostics
Programming and automation experience in Python, Go, or Bash, combined with practical exposure to AI/ML applied to IT operations — anomaly detection, intelligent alerting, or LLM-driven tooling
Hands-on experience with cloud platforms such as AWS or GCP, with an understanding of compute, storage, networking, and IAM in production environments

Preferred Qualifications

Kubernetes, CI/CD, and networking fundamentals — practical experience with container orchestration and deployments, pipeline tooling (ArgoCD, Jenkins, GitHub Actions, or similar), and a grasp of HTTP, DNS, TCP/IP, and how traffic flows through distributed systems
Experience operating systems instrumented with Grafana, Prometheus, Kibana, or equivalent, alongside practical IaC skills using Terraform, Pulumi, or Crossplane
SRE principles applied in practice: error budgets, SLOs, SLIs, toil measurement and reduction
Experience with relational databases in production environments — MySQL or PostgreSQL
BS or MS in Computer Science, Software Engineering, or equivalent technical discipline; equivalent professional experience will be fully considered