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Clearwater Analytics

Cloud Engineer

Actively Reviewing

Clearwater Analytics

Noida Full-Time 4–8 yrs exp Posted 1 month ago  · Apply by Jul 18, 2026
We are looking for a skilled Cloud Engineer to join our Platform Engineering team and help build, maintain, and scale the secure, reliable, and high-performance cloud infrastructure that powers Clearwater core SaaS platform. You will work hands-on across our multi-account AWS environment, contribute to Amazon EKS platform operations, and collaborate with product teams to deliver robust cloud solutions.

Key Responsibilities:

Cloud Infrastructure & Operations:

  • Build, deploy, and maintain scalable, secure, and highly available cloud infrastructure on AWS across multi-account environments.
  • Implement and maintain Infrastructure as Code (IaC) using Terraform and AWS CloudFormation, contributing to reusable modules used across product teams.
  • Optimize cloud environments for cost, performance, and reliability, supporting FinOps practices including Savings Plans, Spot strategy, and Graviton adoption.
  • Collaborate with engineering, data, and security teams to support resilient distributed systems.
  • Participate in continuous improvement initiatives across the platform.
  • Own incident response: on-call rotation, triage, mitigation, and blameless post-mortems.
  • Provide Cloud and Infrastructure support across platform teams.

Kubernetes & EKS:

  • Deploy, operate, and maintain Amazon EKS clusters in a multi-tenant production environment.
  • Support cluster upgrades, patching, and Kubernetes version lifecycle activities.
  • Contribute to internal Helm chart libraries and GitOps-driven cluster configuration using ArgoCD or Flux.

Security & Reliability:

  • Implement zero-trust network principles and enforce IAM least-privilege across AWS accounts.
  • Support SRE practices: contribute to SLO definitions and monitoring for EKS, API Gateway, and related services.
  • Participate in incident response, postmortem analysis, and blameless RCA processes for platform-level issues.
  • Support chaos engineering exercises and disaster recovery testing across availability zones and regions.

Collaboration & Growth:

  • Partner with software engineering teams to deliver end-to-end solutions from design through production.
  • Evaluate new AWS services and open-source tooling to improve infrastructure capabilities.

Required Qualifications:

  • Hands-on experience with AWS cloud services: EC2, VPC, IAM, EKS, S3, CloudWatch, API Gateway, Route 53, and more.
  • Experience operating Amazon EKS in production: cluster lifecycle, RBAC, IRSA, node groups, and autoscaling.
  • Proficiency in Infrastructure as Code with Terraform and AWS CloudFormation.
  • Solid understanding of containerization: Docker, Kubernetes architecture, and container lifecycle management.
  • Experience with monitoring and logging tools: Prometheus, Grafana, Dynatrace, OpenSearch, ELK/Loki.
  • Strong Linux/Unix systems administration and scripting in Bash, Python, or similar.
  • Good knowledge of cloud security best practices: IAM, RBAC, secrets management, and network security.
  • Experience with Helm and GitOps tools (ArgoCD, Flux).
  • Solid networking fundamentals: VPCs, subnets, load balancing, DNS, and Kubernetes ingress controllers.
  • Ability to troubleshoot distributed systems and debug complex production issues.
  • Strong problem-solving skills and the ability to work effectively in a fast-paced team environment.

Preferred Skills:

  • AWS Certifications: Solutions Architect Associate/Professional or DevOps Engineer Professional.
  • Kubernetes Certifications: CKA or CKAD.
  • Experience with Karpenter for EKS node provisioning.
  • Exposure to microservices architecture and distributed systems at financial-services scale.
  • Experience with AWS API Gateway and Lambda Authorizers for JWT/OIDC-based auth flows.
  • Background in cost optimization and performance tuning (Graviton, Spot, Savings Plans).
  • Familiarity with identity federation: OIDC, OAuth2, SAML, Auth0 integration.
  • Understanding of AI/ML infrastructure: model training pipelines, deployment on EKS, and model monitoring.