Mid-Level Google Cloud Platform (GCP) Engineer

RIVA Solutions Inc.

3d•$130,000•Remote

About The Position

RIVA Solutions is seeking a Google Cloud Platform (GCP) Engineer to design, implement, and support cloud-native solutions that advance NOAA NMFS modernization initiatives. The position supports cloud migration, data pipelines, infrastructure automation, containerized applications, and secure cloud operations within an Agile environment.

Requirements

Bachelor's degree in Computer Science, Information Technology, or related field (or equivalent experience).
3–5 years of Google Cloud Platform engineering experience.
GCP Associate Cloud Engineer certification required; Professional Cloud Architect or Professional Data Engineer preferred.
Experience with Compute Engine, GKE, Cloud Run, Cloud Storage, BigQuery, Pub/Sub, Dataflow, and Cloud Functions.
Experience with Terraform, Docker, Kubernetes, Cloud Build, GitHub Actions, and Infrastructure as Code.
Python and/or Go programming experience.
Experience designing IAM, VPCs, and secure cloud networking.
Experience supporting FedRAMP-compliant cloud environments and Agile delivery teams.
Ability to obtain and maintain a Public Trust clearance.

Nice To Haves

Experience supporting NOAA, NMFS, or other Federal civilian agencies.
Vertex AI and machine learning pipeline implementation.
Apigee API management and Cloud Composer (Airflow).
Hybrid cloud experience with AWS and/or Azure.
Experience with Dataplex or enterprise data governance platforms.
Knowledge of FISMA, ATO processes, and NIST 800-53 controls.
Experience with marine science and geospatial data formats including NetCDF, GeoTIFF, and HDF5.

Responsibilities

Assess legacy systems and develop migration strategies for Google Cloud Platform.
Design cloud landing zones, VPC architectures, and enterprise GCP configurations.
Support lift-and-shift, re-platforming, and cloud-native modernization using Anthos, GKE, and Compute Engine.
Apply cloud financial management best practices to optimize cloud utilization and cost.
Develop scalable data pipelines using BigQuery, Dataflow, Pub/Sub, and Cloud Composer.
Design cloud data lakes and data warehouse solutions supporting scientific and operational analytics.
Implement data governance, lineage, and quality controls using Google Cloud services.
Collaborate with data scientists to operationalize machine learning models using Vertex AI.
Develop CI/CD pipelines with Cloud Build, Artifact Registry, and GitHub Actions.
Automate infrastructure provisioning with Terraform and Cloud Deployment Manager.
Implement security controls supporting FISMA, FedRAMP, and NIST 800-53 compliance.
Deploy containerized applications using GKE and Cloud Run.
Develop REST APIs and event-driven services using Cloud Functions and Apigee.
Implement monitoring, logging, tracing, and participate in Agile ceremonies.