Cloud DevOps Engineer

1. 1
   Build Cloud + Container Foundation

   AWS Solutions Architect Associate (or Azure equivalent) combined with Docker proficiency is the starting point. These provide the cloud services knowledge and container fundamentals required for cloud DevOps work. Build hands-on projects: containerized deployments on ECS, serverless functions with Lambda, automated infrastructure with CloudFormation or Terraform.

   AWS SAA or Azure Admin + Docker
2. 2
   Master Kubernetes on Cloud (EKS/AKS/GKE)

   Managed Kubernetes services — AWS EKS, Azure AKS, Google GKE — are the standard deployment platform for containerized workloads. Cloud DevOps engineers provision and manage these clusters, configure networking (Ingress, service mesh), set up autoscaling, and manage cluster upgrades. CKA certification is the essential credential. Build a project deploying a multi-service application to EKS using Helm charts and ArgoCD.

   CKA + EKS/AKS managed Kubernetes
3. 3
   Implement CI/CD on Cloud Platforms

   Cloud-native CI/CD: AWS CodePipeline + CodeBuild + CodeDeploy for AWS-native pipelines; Azure Pipelines + Azure Container Registry for Microsoft environments; GitHub Actions for cloud-agnostic pipelines (most broadly applicable). Build a pipeline that tests, builds a Docker image, pushes to ECR, and deploys to EKS using a Helm chart — this is the typical cloud DevOps pipeline pattern.

   GitHub Actions + cloud-native CI/CD pipeline
4. 4
   Learn Terraform for Cloud Infrastructure

   Terraform is the de facto standard for cloud infrastructure as code across all three major cloud providers. AWS-specific: Terraform for VPCs, EKS clusters, RDS instances, and IAM. Use Terraform Cloud or Atlantis for collaborative state management. HashiCorp Terraform Associate certification ($70) provides the foundational credential.

   Terraform + HashiCorp Certified Terraform Associate
5. 5
   Earn Cloud DevOps Certification

   AWS DevOps Engineer Professional (DOP-C02, $300) or Microsoft DevOps Engineer Expert (AZ-400, $165). AWS DOP validates: CI/CD pipeline design, infrastructure as code, monitoring and logging, and incident response on AWS. AZ-400 validates Azure DevOps: Azure Pipelines, source control, security, and continuous feedback. Choose based on your target employer's cloud platform.

   AWS DevOps Pro or Azure DevOps Expert

• 9:00 AMMorning checks — review GitHub Actions pipeline health, CloudWatch alarms, and EKS cluster status. One deployment failed overnight: a Helm chart misconfiguration in the staging environment. Roll back, fix the values.yaml, re-deploy successfully.
• 10:00 AMNew service onboarding — a new microservice needs its deployment pipeline. Create: GitHub Actions workflow (test → build Docker image → push to ECR → deploy to EKS via Helm), Terraform for the RDS instance and IAM roles, and the Kubernetes namespace and RBAC configuration. Everything as code, nothing manually configured.
• 11:30 AMCost optimization — monthly AWS cost review. EC2 Reserved Instance coverage at 62% — below the 80% target. Analyze the last 30 days of usage patterns. Recommend 1-year RIs for 12 steady-state worker nodes: $14,400 annual savings.
• 12:00 PMLunch — 30 minutes.
• 1:00 PMSecurity scanning — integrate Trivy container image scanning into the CI pipeline. Any HIGH or CRITICAL CVE findings in the Docker image will fail the build. Test with a known-vulnerable base image to verify the gate works, then enable for all production pipelines.
• 3:00 PMEKS upgrade — upgrade the staging EKS cluster from 1.28 to 1.29. Pre-upgrade: check for deprecated API versions in existing manifests, verify add-on compatibility (CoreDNS, kube-proxy, AWS Load Balancer Controller). Perform the upgrade via eksctl, validate all workloads are healthy.
• 5:00 PMDocumentation — update the runbooks for the deployment pipeline and EKS upgrade procedure. Good runbooks mean faster incident recovery and better on-call handoffs.

Cloud DevOps engineers build and operate cloud-native CI/CD pipelines and infrastructure automation — roles where AI tools assist code generation but the system design, troubleshooting, and optimization work requires human expertise. The +19% growth reflects cloud adoption momentum.