Job Summary
We are seeking a skilled and proactive GCP Cloud Engineer with strong expertise in Google Kubernetes Engine (GKE) and Google Cloud Dataproc. The ideal candidate will be responsible for designing, implementing, and managing cloud infrastructure and data processing pipelines on Google Cloud Platform. This role will support scalable deployment of applications and big data workflows, ensuring high availability, security, and performance.
Key Responsibilities
- Design, deploy, and manage containerized applications using Google Kubernetes Engine (GKE).
- Implement and optimize Dataproc clusters for batch and stream data processing.
- Develop and maintain CI/CD pipelines for container-based workloads.
- Collaborate with development and data engineering teams to design scalable, cloud-native solutions.
- Monitor and troubleshoot cloud infrastructure, ensuring high availability and reliability.
- Automate infrastructure provisioning using tools like Terraform, Deployment Manager, or Ansible.
- Ensure security and compliance standards are met across all GCP environments.
- Optimize performance and cost-efficiency of cloud resources.
- Participate in incident response and root cause analysis.
Requirements
Required Skills and Qualifications
- Hands-on experience in cloud engineering with GCP.
- Strong experience with GKE (setup, maintenance, monitoring, scaling).
- Proficiency in Google Cloud Dataproc, including Spark and Hadoop job management.
- Solid understanding of Kubernetes concepts, Helm, and container orchestration.
- Familiarity with CI/CD tools (e.g., Jenkins, GitLab CI/CD, Cloud Build).
- Scripting and automation skills in Python, Shell, or similar.
- Experience with Infrastructure as Code (IaC) tools like Terraform.
- Strong knowledge of cloud networking, IAM, monitoring (Stackdriver/Cloud Monitoring), and logging.
Preferred Qualifications
- GCP Professional Cloud Architect or GCP DevOps Engineer certification.
- Experience with other GCP services like BigQuery, Cloud Functions, Pub/Sub, etc.
- Background in data engineering or DevOps practices.