Job Description

(Remote location) We are seeking a highly skilled GCP DevOps Engineer to join our team. This role requires extensive experience in managing GCP environments, integrating Vertex AI, optimizing BigQuery storage solutions, proficiency with Docker, Kubernetes, ArgoCD, Bitbucket, and implementing Google Logging.


### Key Responsibilities

#### GCP Platform Management

• Architecture & Management: Architect and manage GCP environments, including compute, storage, and networking components.
• Cross-functional Collaboration: Work with cross-functional teams to understand requirements and provide scalable infrastructure solutions.

#### Docker

• Containerization: Design and manage containerized applications using Docker.
• Image Management: Create and maintain Docker images, ensuring performance and security.
• Best Practices: Implement best practices for container orchestration and management.

#### Kubernetes

• Cluster Management: Deploy and manage Kubernetes clusters for orchestrating Docker containers.
• Automation: Automate deployment, scaling, and operations of application containers.
• Helm Charts: Implement and manage Helm charts for application deployment.
• High Availability & DR: Ensure high availability and disaster recovery in Kubernetes environments.

#### ArgoCD

• GitOps Workflows: Implement and manage GitOps workflows using ArgoCD.
• Automation: Automate application deployments and updates in Kubernetes.
• Synchronization: Ensure continuous delivery and synchronization of application states between Git repositories and Kubernetes clusters.

#### Bitbucket

• Source Code Management: Manage source code repositories using Bitbucket.
• CI/CD Pipelines: Implement CI/CD pipelines using Bitbucket Pipelines.
• Integration: Collaborate with development teams to integrate Bitbucket with other CI/CD tools and processes.

#### Vertex AI Integration

• Collaboration with AI Specialists: Work with data scientists and AI specialists to integrate and optimize solutions using Vertex AI.
• ML Pipeline Management: Implement and manage machine learning pipelines and models within the Vertex AI environment.

#### Google Logging

• Logging Implementation: Implement Google Cloud Logging to monitor and analyze logs from various GCP services.
• Dashboard Creation: Create and maintain logging dashboards to provide insights and monitor system performance.
• Alerting: Set up alerting based on log data to proactively identify and resolve issues.

#### BigQuery Storage

• Data Storage Design: Design and optimize data storage solutions using BigQuery Storage.
• Collaboration with Data Teams: Work with data engineers and analysts to ensure efficient data processing and analysis.

#### Security and Privacy

• Measures Implementation: Implement and maintain security measures like data encryption and access controls.
• Regulatory Compliance: Ensure compliance with relevant regulations throughout the software development lifecycle.

#### Problem Solving

• Technical Issue Resolution: Identify and resolve technical issues to align software solutions with business goals.

#### Testing and Quality Assurance

• Testing Implementation: Implement unit tests, integration tests, and automated testing to maintain software quality.

#### Documentation

• Comprehensive Documentation: Produce and update documentation for code, software architecture, and development processes.

### Qualifications

Required Experience:

• GCP & IaC Tools: 6+ years with GCP and Infrastructure-as-Code (IaC) tools like Terraform.
• Docker: Extensive experience with Docker, including creating and maintaining images and implementing orchestration best practices.
• Kubernetes: Proficiency with Kubernetes for automating deployment and managing Helm charts.
• ArgoCD: Experience implementing and managing GitOps workflows using ArgoCD.
• Bitbucket: Proficiency with Bitbucket for source code management and CI/CD pipeline implementation.
• Vertex AI & BigQuery: Experience with Vertex AI and BigQuery storage solutions.
• Google Logging: Experience implementing and managing Google Cloud Logging.
• Security Measures: Implementing security measures like encryption and access controls.
• Technical Issue Resolution: Identifying and resolving intricate technical issues.