Hands-on design, implementation, and operation of Kubernetes clusters (AKS, EKS, GKE) including upgrades, scaling, node pools, networking, and cluster add-ons.
Hands-on deployment and management of containerized workloads, including Helm charts, Kustomize, operators, service mesh, ingress controllers, and autoscaling.
Develop and maintain Terraform modules for Kubernetes clusters, cloud resources, and platform components.
Build, optimize, and maintain CI/CD pipelines (GitHub Actions, Azure Pipelines) specifically for containerized applications and GitOps workflows.
Implement Kubernetes-native automation such as admission controllers, policy engines, automated remediation, and cluster governance.
Define and enforce DevOps processes for build, test, release, and production operations in a Kubernetes-first environment.
Lead hands-on incident management and root-cause analysis for cluster failures, workload issues, networking problems, and cloud infrastructure incidents.
Work directly with application teams to onboard workloads to Kubernetes: pod design, resource sizing, capacity planning, troubleshooting, and performance tuning.
Implement security as code across clusters: RBAC, Pod Security Standards, network policies, image scanning, compliance automation, and secret management.
Mentor engineers through hands-on guidance, code reviews, architecture reviews, and troubleshooting sessions.
Support data workloads, including Databricks deployments integrated with cloud and Kubernetes environments.
Skills & Qualifications
Bachelors degree in computer science, information technology, or related field.
Fluent in English.
9+ years of hands-on experience designing and operating cloud solutions on AWS, Azure, and GCP.
Deep hands-on Kubernetes experience (AKS, EKS, GKE) including cluster lifecycle, workload orchestration, networking, storage, and security.
Advanced Terraform experience, building reusable modules and multi-cloud provisioning.
Hands-on CI/CD experience with GitHub Actions, Azure Pipelines, and container-focused delivery pipelines.
Strong scripting experience with PowerShell, Bash, Ansible, Chef, or Puppet.
Solid Linux fundamentals and container runtime knowledge.
Good understanding of programming languages such as Python.
Hands-on experience with Databricks and cloud-native data engineering environments.
Excellent troubleshooting skills across Kubernetes, cloud infrastructure, and distributed systems.
