What are the responsibilities and job description for the GCP Data Engineer (Hybrid: Fort Lauderdale, Irving, NJ or NY) position at Braintrust?
Responsibilities
Design, develop, and maintain reusable Terraform modules for enterprise cloud environments.
Implement Infrastructure as Code (IaC) best practices across development, testing, staging, and production environments.
Provision and manage GCP infrastructure using Terraform and automation frameworks.
Build complete cloud environments from scratch using GCP services and infrastructure automation.
Design, deploy, and manage cloud-native solutions leveraging GCP services.
Configure and maintain Cloud Run, BigQuery, Cloud Storage, Firestore, IAM, Secret Manager, and Load Balancing services.
Develop and maintain CI/CD pipelines for infrastructure and application deployments.
Experience in GCP-native data platforms (BigQuery, Dataflow, Pub/Sub, Cloud Storage, Cloud Composer
Implement infrastructure versioning, automated code reviews, governance controls, and deployment strategies.
Ensure platform reliability, scalability, high availability, and disaster recovery readiness.
Configure and manage networking components including VPCs, Subnets, Firewall Rules, Private Connectivity, DNS, and SSL Certificates.
Manage Identity and Access Management (IAM), Service Accounts, and security controls.
Implement monitoring, alerting, logging, and observability solutions using Cloud Monitoring and related tools.
Experience building APIs and data connectors (Python, Cloud Functions, Cloud Run) that serve downstream applications or AI/ML systems
Proficiency with IaC (Terraform) and CI/CD (Cloud Build, GitHub Actions, or equivalent) for pipeline deployment
Experience with GCP IAM, VPC networking, and container platforms (GKE, Cloud Run)
Troubleshoot production issues and execute root cause analysis for critical incidents.
Develop operational runbooks, architecture documentation, and support procedures.
Collaborate with Data Engineering and AI/ML teams to build secure and scalable data and AI platforms.
Support infrastructure required for Vertex AI Agent Engine, Vertex AI Search, Model Armor, and AI-driven workloads.
Implement security, compliance, and governance standards across cloud environments.
Design, develop, and maintain reusable Terraform modules for enterprise cloud environments.
Implement Infrastructure as Code (IaC) best practices across development, testing, staging, and production environments.
Provision and manage GCP infrastructure using Terraform and automation frameworks.
Build complete cloud environments from scratch using GCP services and infrastructure automation.
Design, deploy, and manage cloud-native solutions leveraging GCP services.
Configure and maintain Cloud Run, BigQuery, Cloud Storage, Firestore, IAM, Secret Manager, and Load Balancing services.
Develop and maintain CI/CD pipelines for infrastructure and application deployments.
Experience in GCP-native data platforms (BigQuery, Dataflow, Pub/Sub, Cloud Storage, Cloud Composer
Implement infrastructure versioning, automated code reviews, governance controls, and deployment strategies.
Ensure platform reliability, scalability, high availability, and disaster recovery readiness.
Configure and manage networking components including VPCs, Subnets, Firewall Rules, Private Connectivity, DNS, and SSL Certificates.
Manage Identity and Access Management (IAM), Service Accounts, and security controls.
Implement monitoring, alerting, logging, and observability solutions using Cloud Monitoring and related tools.
Experience building APIs and data connectors (Python, Cloud Functions, Cloud Run) that serve downstream applications or AI/ML systems
Proficiency with IaC (Terraform) and CI/CD (Cloud Build, GitHub Actions, or equivalent) for pipeline deployment
Experience with GCP IAM, VPC networking, and container platforms (GKE, Cloud Run)
Troubleshoot production issues and execute root cause analysis for critical incidents.
Develop operational runbooks, architecture documentation, and support procedures.
Collaborate with Data Engineering and AI/ML teams to build secure and scalable data and AI platforms.
Support infrastructure required for Vertex AI Agent Engine, Vertex AI Search, Model Armor, and AI-driven workloads.
Implement security, compliance, and governance standards across cloud environments.