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DevOps Engineer – API Platform / GCP
Position: DevOps Engineer – API Platform / GCP
Company: Global Professional Services Organization
Client Industry: Transportation
Location: Dallas, TX or San Antonio, TX
Work Arrangement: Hybrid – Onsite at Client HQ 2 Days per Week
Employment Type: W2 Contract-to-Hire
Engagement Requirement: W2 Candidates Only; C2C Is Not Permitted
Technical Focus: CI/CD, DevOps, Google Cloud Platform (GCP), API Deployment, Jenkins, GitHub/GitLab, Terraform, Kubernetes, and Scripting
Overview
The DevOps Engineer – API Platform / GCP will design, develop, automate, and maintain the infrastructure and deployment processes supporting enterprise API applications for a transportation-industry client of a global professional services organization.
This is a hands-on engineering position focused on building reliable CI/CD pipelines, automating Google Cloud infrastructure, supporting Kubernetes-based deployments, and improving the consistency and reliability of API releases. The engineer will work with Jenkins, GitHub or GitLab, Terraform, Kubernetes, and scripting technologies to reduce manual intervention, improve deployment efficiency, and troubleshoot infrastructure and release-related problems.
The role requires an engineer who can take ownership of deployment automation from source-code integration through infrastructure provisioning, application deployment, validation, and operational support. The individual will collaborate with application development, platform engineering, infrastructure, and client stakeholders in a hybrid delivery environment.
The ultimate performance standard is the ability to take an API application from a committed code change through an automated, validated, and operationally supportable deployment on Google Cloud Platform.
Purpose:
Help build and strengthen the cloud infrastructure and deployment automation supporting critical enterprise API applications for a transportation-industry client. Your work will directly contribute to reliable application delivery, operational stability, and the ability of engineering teams to deploy changes efficiently and consistently.
This opportunity is well suited to a hands-on DevOps engineer who enjoys building automation, troubleshooting difficult infrastructure problems, and improving the reliability of software delivery. The role provides meaningful technical ownership and the opportunity to see measurable results through successful deployments, reduced manual effort, and improved operational processes.
Candidate Requirements
Objectives
1. Establish and Optimize CI/CD Pipelines for Enterprise API Deployments. Within the first 30–90 days, assess the client's existing source-control, build, and deployment processes and develop or enhance CI/CD pipelines using the selected tools, including Jenkins, GitHub, or GitLab. Automate appropriate build, validation, and deployment activities to support consistent API releases across the available environments. Identify manual steps, recurring failures, and pipeline dependencies while collaborating with development and platform engineering teams to implement improvements. Success will be measured by functioning automated pipelines, successful execution of representative deployments, reduced manual intervention, and documented improvement against the initial deployment baseline.
2. Automate and Maintain GCP Infrastructure Using Terraform. Within the first 60–120 days, develop or improve Terraform-based Infrastructure as Code capabilities supporting the client's API deployment environment on Google Cloud Platform. Implement reusable and maintainable infrastructure configurations consistent with approved architectural, security, and operational standards. Validate infrastructure changes through established review and deployment processes and address configuration inconsistencies that create deployment or operational risks. Success will be measured by successful automated provisioning or modification of assigned infrastructure, repeatable infrastructure changes, configuration consistency, and reduced manual provisioning effort.
3. Improve Kubernetes Deployment Reliability and Operational Support. Within the first 90–180 days, improve the consistency, reliability, and supportability of Kubernetes-based application deployments supporting the client's API platform. Configure, troubleshoot, and enhance assigned deployment workflows while identifying problems involving container configuration, resource allocation, application startup, service connectivity, and environment dependencies. Collaborate with development, infrastructure, and platform engineering teams to resolve deployment failures and implement durable corrective actions. Success will be measured by successful Kubernetes deployments, timely resolution of deployment-related defects, reduced recurrence of known failures, and improved operational stability against agreed service expectations.
4. Strengthen End-to-End API Deployment Automation and DevOps Practices. Within the first 180 days, establish or improve an integrated API deployment process connecting source control, CI/CD tooling, Terraform infrastructure automation, Kubernetes, and GCP services. Develop supporting scripts, standardize repeatable procedures, and document the processes required to operate and troubleshoot the deployment environment. Work with client stakeholders and engineering teams to identify release dependencies, manage deployment risks, and improve the reliability of the software delivery lifecycle. Success will be measured by demonstrated end-to-end API deployment capability, improved deployment consistency, fewer manual handoffs, and successful resolution of infrastructure and release issues.
Subtasks
1. Assess the Existing GCP and API Deployment Environment. During the first 15–30 days, review the client's existing GCP infrastructure, API deployment architecture, source-control repositories, CI/CD processes, Kubernetes environments, and Terraform configurations where available. Identify current deployment workflows, manual dependencies, recurring issues, and technical constraints that could affect reliable application delivery. Establish working relationships with development, platform engineering, infrastructure, and client stakeholders to understand delivery expectations and operational priorities.
2. Configure and Improve Source-Control and CI/CD Workflows. During the first 30–60 days, implement or enhance automated workflows using the client's selected Jenkins, GitHub, or GitLab tooling. Configure appropriate build, validation, deployment, and failure-handling stages for assigned API applications while following established engineering and access-control standards. Develop scripts to eliminate repetitive tasks and improve pipeline consistency across supported environments
3. Develop Terraform-Based Infrastructure Automation. During the first 30–90 days, create or improve Terraform configurations and reusable components for provisioning and maintaining assigned GCP resources. Ensure infrastructure changes are version-controlled, reviewable, repeatable, and aligned with the client's approved cloud architecture and operational requirements. Troubleshoot provisioning failures and configuration inconsistencies while improving the maintainability of existing infrastructure code
4. Configure and Troubleshoot Kubernetes Deployments. During the first 60–120 days, support the deployment of assigned API workloads to Kubernetes environments and address problems affecting application rollout or runtime readiness. Investigate deployment failures, container configuration issues, resource constraints, and service connectivity problems using appropriate platform diagnostics. Implement improvements that make deployments easier to validate, troubleshoot, and recover when failures occur. Success will be measured by successful application rollouts, timely resolution of Kubernetes deployment issues, and improved deployment reliability.
5. Automate End-to-End API Release Processes. During the first 90–150 days, integrate CI/CD pipelines, Terraform workflows, Kubernetes deployment activities, and required scripting into a repeatable API release process. Validate the movement of application changes through supported environments while maintaining appropriate release controls and responding effectively to deployment failures. Identify opportunities to simplify deployment handoffs, improve release consistency, and reduce the number of manual actions required to complete a release. Success will be measured by successful end-to-end automated deployments, reduced manual release steps, and consistent execution of the approved deployment process.
6. Stabilize Operations and Document Deployment Procedures. During the first 120–180 days, investigate recurring deployment and infrastructure problems and implement durable corrective actions within the assigned API platform environment. Develop practical documentation covering CI/CD execution, Terraform operations, Kubernetes deployments, troubleshooting procedures, and appropriate recovery steps. Share implementation knowledge with client and engineering stakeholders to improve the consistency and supportability of ongoing operations.
Long-Term Success:
The engineer is recognized as a reliable technical owner of API deployment automation on GCP, capable of building, improving, troubleshooting, and sustaining the infrastructure and pipelines that support consistent application delivery.
Company: Global Professional Services Organization
Client Industry: Transportation
Location: Dallas, TX or San Antonio, TX
Work Arrangement: Hybrid – Onsite at Client HQ 2 Days per Week
Employment Type: W2 Contract-to-Hire
Engagement Requirement: W2 Candidates Only; C2C Is Not Permitted
Technical Focus: CI/CD, DevOps, Google Cloud Platform (GCP), API Deployment, Jenkins, GitHub/GitLab, Terraform, Kubernetes, and Scripting
Overview
The DevOps Engineer – API Platform / GCP will design, develop, automate, and maintain the infrastructure and deployment processes supporting enterprise API applications for a transportation-industry client of a global professional services organization.
This is a hands-on engineering position focused on building reliable CI/CD pipelines, automating Google Cloud infrastructure, supporting Kubernetes-based deployments, and improving the consistency and reliability of API releases. The engineer will work with Jenkins, GitHub or GitLab, Terraform, Kubernetes, and scripting technologies to reduce manual intervention, improve deployment efficiency, and troubleshoot infrastructure and release-related problems.
The role requires an engineer who can take ownership of deployment automation from source-code integration through infrastructure provisioning, application deployment, validation, and operational support. The individual will collaborate with application development, platform engineering, infrastructure, and client stakeholders in a hybrid delivery environment.
The ultimate performance standard is the ability to take an API application from a committed code change through an automated, validated, and operationally supportable deployment on Google Cloud Platform.
Purpose:
Help build and strengthen the cloud infrastructure and deployment automation supporting critical enterprise API applications for a transportation-industry client. Your work will directly contribute to reliable application delivery, operational stability, and the ability of engineering teams to deploy changes efficiently and consistently.
This opportunity is well suited to a hands-on DevOps engineer who enjoys building automation, troubleshooting difficult infrastructure problems, and improving the reliability of software delivery. The role provides meaningful technical ownership and the opportunity to see measurable results through successful deployments, reduced manual effort, and improved operational processes.
Candidate Requirements
- Hands-On DevOps and CI/CD Engineering: Demonstrated experience designing, implementing, maintaining, and troubleshooting CI/CD pipelines supporting application deployments. Practical experience with Jenkins, GitHub, or GitLab is important, including the ability to automate build, validation, and deployment activities.
- Google Cloud Platform: Hands-on experience working with GCP infrastructure supporting cloud-based application deployment and operations. Candidates should be comfortable troubleshooting infrastructure and environment-related issues affecting application delivery.
- Terraform and Infrastructure as Code: Demonstrated ability to create, modify, maintain, and troubleshoot Terraform configurations supporting repeatable cloud infrastructure provisioning and management.
- Kubernetes: Practical experience deploying and troubleshooting containerized applications in Kubernetes environments. Candidates should understand the relationship between application configuration, container behavior, infrastructure resources, and successful deployment.
- Scripting and Automation: Strong scripting capability to automate routine tasks, integrate engineering tools, troubleshoot workflows, and improve deployment efficiency. The source JD does not specify a mandatory scripting language.
- API Deployment Experience: Experience supporting the deployment of APIs or API-based applications, including understanding the dependencies between application code, infrastructure, CI/CD pipelines, and runtime environments.
- Cross-Functional Collaboration: Ability to work effectively with application developers, DevOps, platform engineers, infrastructure teams, and client stakeholders to resolve problems and deliver reliable solutions.
Objectives
1. Establish and Optimize CI/CD Pipelines for Enterprise API Deployments. Within the first 30–90 days, assess the client's existing source-control, build, and deployment processes and develop or enhance CI/CD pipelines using the selected tools, including Jenkins, GitHub, or GitLab. Automate appropriate build, validation, and deployment activities to support consistent API releases across the available environments. Identify manual steps, recurring failures, and pipeline dependencies while collaborating with development and platform engineering teams to implement improvements. Success will be measured by functioning automated pipelines, successful execution of representative deployments, reduced manual intervention, and documented improvement against the initial deployment baseline.
2. Automate and Maintain GCP Infrastructure Using Terraform. Within the first 60–120 days, develop or improve Terraform-based Infrastructure as Code capabilities supporting the client's API deployment environment on Google Cloud Platform. Implement reusable and maintainable infrastructure configurations consistent with approved architectural, security, and operational standards. Validate infrastructure changes through established review and deployment processes and address configuration inconsistencies that create deployment or operational risks. Success will be measured by successful automated provisioning or modification of assigned infrastructure, repeatable infrastructure changes, configuration consistency, and reduced manual provisioning effort.
3. Improve Kubernetes Deployment Reliability and Operational Support. Within the first 90–180 days, improve the consistency, reliability, and supportability of Kubernetes-based application deployments supporting the client's API platform. Configure, troubleshoot, and enhance assigned deployment workflows while identifying problems involving container configuration, resource allocation, application startup, service connectivity, and environment dependencies. Collaborate with development, infrastructure, and platform engineering teams to resolve deployment failures and implement durable corrective actions. Success will be measured by successful Kubernetes deployments, timely resolution of deployment-related defects, reduced recurrence of known failures, and improved operational stability against agreed service expectations.
4. Strengthen End-to-End API Deployment Automation and DevOps Practices. Within the first 180 days, establish or improve an integrated API deployment process connecting source control, CI/CD tooling, Terraform infrastructure automation, Kubernetes, and GCP services. Develop supporting scripts, standardize repeatable procedures, and document the processes required to operate and troubleshoot the deployment environment. Work with client stakeholders and engineering teams to identify release dependencies, manage deployment risks, and improve the reliability of the software delivery lifecycle. Success will be measured by demonstrated end-to-end API deployment capability, improved deployment consistency, fewer manual handoffs, and successful resolution of infrastructure and release issues.
Subtasks
1. Assess the Existing GCP and API Deployment Environment. During the first 15–30 days, review the client's existing GCP infrastructure, API deployment architecture, source-control repositories, CI/CD processes, Kubernetes environments, and Terraform configurations where available. Identify current deployment workflows, manual dependencies, recurring issues, and technical constraints that could affect reliable application delivery. Establish working relationships with development, platform engineering, infrastructure, and client stakeholders to understand delivery expectations and operational priorities.
2. Configure and Improve Source-Control and CI/CD Workflows. During the first 30–60 days, implement or enhance automated workflows using the client's selected Jenkins, GitHub, or GitLab tooling. Configure appropriate build, validation, deployment, and failure-handling stages for assigned API applications while following established engineering and access-control standards. Develop scripts to eliminate repetitive tasks and improve pipeline consistency across supported environments
3. Develop Terraform-Based Infrastructure Automation. During the first 30–90 days, create or improve Terraform configurations and reusable components for provisioning and maintaining assigned GCP resources. Ensure infrastructure changes are version-controlled, reviewable, repeatable, and aligned with the client's approved cloud architecture and operational requirements. Troubleshoot provisioning failures and configuration inconsistencies while improving the maintainability of existing infrastructure code
4. Configure and Troubleshoot Kubernetes Deployments. During the first 60–120 days, support the deployment of assigned API workloads to Kubernetes environments and address problems affecting application rollout or runtime readiness. Investigate deployment failures, container configuration issues, resource constraints, and service connectivity problems using appropriate platform diagnostics. Implement improvements that make deployments easier to validate, troubleshoot, and recover when failures occur. Success will be measured by successful application rollouts, timely resolution of Kubernetes deployment issues, and improved deployment reliability.
5. Automate End-to-End API Release Processes. During the first 90–150 days, integrate CI/CD pipelines, Terraform workflows, Kubernetes deployment activities, and required scripting into a repeatable API release process. Validate the movement of application changes through supported environments while maintaining appropriate release controls and responding effectively to deployment failures. Identify opportunities to simplify deployment handoffs, improve release consistency, and reduce the number of manual actions required to complete a release. Success will be measured by successful end-to-end automated deployments, reduced manual release steps, and consistent execution of the approved deployment process.
6. Stabilize Operations and Document Deployment Procedures. During the first 120–180 days, investigate recurring deployment and infrastructure problems and implement durable corrective actions within the assigned API platform environment. Develop practical documentation covering CI/CD execution, Terraform operations, Kubernetes deployments, troubleshooting procedures, and appropriate recovery steps. Share implementation knowledge with client and engineering stakeholders to improve the consistency and supportability of ongoing operations.
Long-Term Success:
The engineer is recognized as a reliable technical owner of API deployment automation on GCP, capable of building, improving, troubleshooting, and sustaining the infrastructure and pipelines that support consistent application delivery.