описание

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задачи

Define and drive cloud architecture and DevOps strategy
Design, manage, and optimize secure, scalable GCP infrastructure
Build and maintain CI/CD pipelines and Infrastructure as Code (Terraform)
Lead platform engineering initiatives, including Internal Developer Platforms (IDPs) and self-service automation
Drive company-wide FinOps, cloud budgeting, and cost optimization
Establish SRE practices, including SLIs/SLOs, error budgets, and capacity planning
Lead incident response, improve observability, and ensure high availability and disaster recovery
Make architectural decisions and align cross-functional teams
Mentor engineers and promote engineering best practices

7+ Years of experience in DevOps, Infrastructure, or SRE
2+ Years in a Lead, Staff, or Principal-level technical role
Strong hands-on experience with Google Cloud Platform (GCP), including GKE, Cloud Run, IAM, VPC, and other core services
Strong knowledge of Terraform and Infrastructure as Code
Hands-on experience with Docker and Kubernetes/GKE
Proven experience building CI/CD pipelines (GitHub Actions, GitLab CI, Cloud Build, or Jenkins)
Proficiency in Python, Go, or Bash
Strong knowledge of cloud networking, security, IAM, and compliance
Experience with observability tools such as Google Cloud Monitoring, Prometheus, Grafana, or Datadog
Proven experience with incident management, on-call processes, and SRE practices
Experience operating highly available, enterprise-scale production infrastructure
Strong technical leadership, communication, and cross-team collaboration skills
Будет плюсом: GCP certifications (Professional Cloud Architect, Professional Cloud DevOps Engineer, or equivalent), experience with Internal Developer Platforms (Backstage, Port), knowledge of GitOps (Argo CD, Flux) and progressive delivery strategies, experience with event-driven architectures, Kafka, and Pub/Sub, experience with cloud databases, including Cloud SQL, Cloud Spanner, and Firestore, experience with FinOps, cloud budgeting, and unit economics optimization, familiarity with data engineering and data science workloads on GCP

gcp gke terraform kubernetes docker ci/cd python go bash cloud networking iam prometheus grafana finops gitops