<p>Responsibilities</p><ul><li>Architect, deploy, and maintain scalable data infrastructure in GCP with a focus on Cloud Composer (Airflow) and Google Kubernetes Engine (GKE).</li><li>Manage containerized workloads using Docker and Kubernetes, including lifecycle management, networking, monitoring, and troubleshooting. Collaborate with data engineering teams to migrate traditional workloads to containerized platforms.</li><li>Implement and maintain CI/CD pipelines using tools like Jenkins, Git, and Terraform</li><li>Ensure observability and reliability of data pipelines and containerized services using Prometheus, Grafana, and other monitoring tools.</li><li>Apply best practices for container security, image vulnerability scanning, and configuration management.</li><li>Support both open-source and Google-native technologies that interact with Composer and GKE, such as CloudSQL, BigQuery, Pub/Sub, and Artifact Registry Support day to day platform operations</li></ul><p>Required Qualifications</p><ul><li>5+ years of experience with container orchestration platforms (Kubemetes, GKE, AKS, EKS).</li><li>5+ years of hands-on experience with Google Cloud Platform, including IAM, networking, and resource management.</li><li>5+ years of experience deploying and managing Docker containers in production environments.</li><li>Strong understanding of Kubernetes architecture, including control plane components and networking.</li><li>Experience with Python (5+ years), especially in the context of automation and data workflows.</li><li>Experience with Composer (Airflow) DAG development and orchestration.</li><li>Proficiency in Linux-based infrastructure and scripting languages (Bash, Python, Java and GoLang are a plus).</li><li>Familiarity with container monitoring, logging, and troubleshooting tools.</li></ul><p>Preferred Qualifications</p><ul><li>Experience with Helm, Terraform, and Jenkins for deployment automation.</li><li>Exposure to SQL, NoSQL, and CloudSQL databases.</li><li>Experience migrating legacy applications to containerized platforms.</li><li>Knowledge of open-source data tools and Google Cloud services that support large-scale data environments.</li></ul>
Responsibilities
Architect, deploy, and maintain scalable data infrastructure in GCP with a focus on Cloud Composer (Airflow) and Google Kubernetes Engine (GKE).
Manage containerized workloads using Docker and Kubernetes, including lifecycle management, networking, monitoring, and troubleshooting. Collaborate with data engineering teams to migrate traditional workloads to containerized platforms.
Implement and maintain CI/CD pipelines using tools like Jenkins, Git, and Terraform
Ensure observability and reliability of data pipelines and containerized services using Prometheus, Grafana, and other monitoring tools.
Apply best practices for container security, image vulnerability scanning, and configuration management.
Support both open-source and Google-native technologies that interact with Composer and GKE, such as CloudSQL, BigQuery, Pub/Sub, and Artifact Registry Support day to day platform operations
Required Qualifications
5+ years of experience with container orchestration platforms (Kubemetes, GKE, AKS, EKS).
5+ years of hands-on experience with Google Cloud Platform, including IAM, networking, and resource management.
5+ years of experience deploying and managing Docker containers in production environments.
Strong understanding of Kubernetes architecture, including control plane components and networking.
Experience with Python (5+ years), especially in the context of automation and data workflows.
Experience with Composer (Airflow) DAG development and orchestration.
Proficiency in Linux-based infrastructure and scripting languages (Bash, Python, Java and GoLang are a plus).
Familiarity with container monitoring, logging, and troubleshooting tools.
Preferred Qualifications
Experience with Helm, Terraform, and Jenkins for deployment automation.
Exposure to SQL, NoSQL, and CloudSQL databases.
Experience migrating legacy applications to containerized platforms.
Knowledge of open-source data tools and Google Cloud services that support large-scale data environments.