Cloud Operations Engineer, O'Reilly Media

• Bachelor's degree in Computer Science or a related field
• 5+ years of experience working in cloud infrastructure, platform engineering, or a related discipline
• In lieu of degree, equivalent education and/or experience may be considered
• Hands-on experience with Kubernetes in production environments (cluster management, workloads, networking)
• Proficiency with infrastructure-as-code tools, particularly Terraform
• Experience with at least one major cloud provider (GCP, AWS, or Azure)
• Solid scripting and automation skills in Python, Bash, or a comparable language
• Experience with modern observability platforms (Datadog, Grafana, or similar)
• Strong understanding of Linux systems administration
• Working knowledge of CI/CD concepts and tools (GitHub Actions, ArgoCD, Jenkins, or similar)
• Excellent communication skills — you write clearly, ask good questions, and explain complex systems accessibly
• AI-Augmented Development: Has the ability to demonstrate using AI-enabled development tools (e.g., Claude Code, Cursor) to streamline coding, debugging, and infrastructure-as-code authoring

Desirable

• O'Reilly Media's Cloud Operations Engineering team is a diverse group of engineers responsible for the infrastructure, developer platforms, and automation that let our software and business teams focus on delivering business value — without worrying about how or where their code runs
• We operate at the intersection of platform engineering, site reliability, and cloud infrastructure
• We're a collaborative, supportive team that believes in "raising the water level" — giving every engineer the opportunity to grow across our full stack and to actively help their teammates do the same
• As a Cloud Operations Engineer at O'Reilly, you'll work on the systems and tooling that power our learning platform
• This is not a pure ops role — it's a software-forward engineering position where you'll write infrastructure-as-code, build developer tooling, maintain our Kubernetes platform, and contribute to the internal developer experience that hundreds of engineers depend on every day
• You'll operate across what modern organizations call Platform Engineering and SRE: building reusable infrastructure primitives, maintaining production reliability through solid observability practices, and partnering with product engineering teams to enable faster, safer delivery
• Maintaining and updating our Kubernetes cluster to ensure steady-state operations
• Writing or extending Terraform modules to provision and manage cloud infrastructure
• Contributing features to the Python CLI tooling we use to manage infrastructure workflows
• Design, build, and maintain cloud infrastructure using infrastructure-as-code (Terraform) on GCP
• Manage and evolve our Kubernetes platform, including cluster operations, workload configuration, and service mesh (Istio)
• Develop and improve internal tooling that abstracts cloud complexity and improves the developer experience
• Collaborate with product engineering teams to understand service deployment needs and deliver infrastructure solutions
• Monitor platform health using Datadog; proactively identify and resolve performance, availability, and security issues
• Participate in on-call rotation and incident response; drive blameless post-mortems and eliminate recurring issues at their root cause
• Define and track service-level indicators and objectives (SLIs/SLOs) for critical platform components
• Implement and refine alerting, dashboards, and runbooks that reduce mean time to resolution
• Embed security best practices into infrastructure workflows (DevSecOps) — not as an afterthought, but as a design principle
• Help maintain cloud security posture, IAM hygiene, and policy guardrails across our cloud environment
• Stay current with cloud security developments and proactively surface risks to the team
• Execute and maintain our automated disaster recovery processes
• Work closely with product engineering teams to understand their needs and remove infrastructure friction
• Document systems, processes, and architectural decisions clearly so knowledge is shared, not siloed
• Recommend improvements to tooling, architecture, and processes — and help drive them to completion
• Keep current with the evolving cloud-native ecosystem and bring relevant knowledge back to the team