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Lead DevOps Engineer - Google Cloud Platform

Scaling Theory Technologies Pvt Ltd
Multiple Locations
5 - 9 Years

Posted on: 30/10/2025

Job Description

Description :


As the Lead DevOps Engineer, you'll own our cloud infrastructure and CI/CD ecosystem end-to-end. You'll design scalable, secure, and automated cloud systems, mentor engineers, and collaborate closely with backend, data, and product teams to deliver resilient, high-performance environments.


Responsibilities :


- Design, implement, and maintain GCP-based cloud infrastructure (Compute Engine, Cloud Run, GKE, Pub/Sub, Cloud SQL, BigQuery, etc.).


- Build and manage CI/CD pipelines using tools like GitHub Actions, Jenkins, or Cloud Build.


- Automate infrastructure provisioning via Terraform / Helm / Ansible.


- Monitor, optimize, and secure cloud performance, cost, and availability.


- Implement DevSecOps best practices, including IAM policies, secret management, and vulnerability scanning.


- Set up observability stacks (Prometheus, Grafana, ELK, Stackdriver).


- Collaborate with engineering teams to design scalable, fault-tolerant architectures.


- Own incident management and on-call rotations, driving root-cause analysis and uptime improvement.


- Mentor and lead junior DevOps/SRE engineers, setting standards for automation and operational excellence.


Requirements :


- 6+ years of DevOps/SRE experience with 2+ years leading small teams or projects.


- Hands-on expertise with GCP services and Terraform / Infrastructure as Code (IaC).


- Strong command of Linux systems, Docker/Kubernetes, and CI/CD pipelines.


- Proficiency in scripting with Python / Bash / Go.


- Solid understanding of networking, security, and cloud cost optimization.


- Familiarity with monitoring, alerting, and logging tools (Grafana, Datadog, Stackdriver).


- Experience in fast-paced startup environments or scaling systems from MVP to production.


Nice-to-Have :


- GCP certification (Professional DevOps Engineer / Cloud Architect).


- Experience with multi-cloud or hybrid environments.


- Exposure to microservices, service mesh (Istio), or GitOps (ArgoCD, Flux).


- Background in data engineering or ML infrastructure is a plus.


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