Cloud Engineer - GCP, DevOps

Nys Dfs•New York City, NY

1d•Hybrid

About The Position

The Mid-Level Cloud Engineer is a hands-on engineering role on the newly established Client Platform Services team. The role contributes to building, operating, and maintaining an internal platform that helps development and operations teams deliver software more quickly and reliably. The engineer works under the guidance of Lead and Senior Platform Engineers in a structured, mentorship-rich environment. The position is a 30-month contract based in New York, NY, with a work model of 50% telecommuting and 50% in-office work per week. Interviews are virtual.

Requirements

60 months of experience in cloud infrastructure, DevOps, or systems administration, including at least 2 years in a public cloud environment; GCP is preferred, while AWS or Azure are considered.
36 months of experience with GCP, including Compute Engine, Cloud Storage, VPC networking, IAM, and Secret Manager.
36 months of experience with Terraform, including reading, writing, and modifying infrastructure code within established patterns.
Bachelor's degree.
Google Cloud Associate Cloud Engineer or Terraform Associate (HashiCorp) certification.
36 months of experience building and maintaining CI/CD pipelines using Cloud Build, GitHub Actions, or an equivalent.
36 months of experience with GitHub and Git workflows, including pull requests and branch management.
24 months of experience in an Agile delivery environment, including sprint ceremonies and iterative delivery.
12 months of experience with DataDog for log analysis and observability.
12 months of experience with static code analysis tools such as SonarQube within a CI/CD pipeline.

Responsibilities

Build and maintain CI/CD pipelines that support development team delivery workflows.
Author and maintain Terraform code for GCP infrastructure provisioning, following established standards and patterns.
Support and maintain existing Salesforce/GearSet deployment pipelines, including troubleshooting and documentation.
Maintain the SonarQube and GitHub integration for static code analysis and security scanning.
Manage build artifacts using GCP Artifact Registry.
Support the observability stack, including DataDog log forwarding, monitoring, and alerting.
Support GKE cluster operations, including workload deployments and namespace management.
Manage GCP resources, including compute, networking, IAM, and secrets management across development, test, and production environments.
Migrate manually provisioned GCP resources into Terraform.
Participate in Agile ceremonies and sprint delivery.