Cloud and DevOps Engineer (w/Google Cloud Platform) - Michigan onsite
Indexed description
"Strong knowledge of cloud platforms (*Google Cloud*)
- Proficiency in Infrastructure as Code (IaC) tools (e.g., Terraform, CloudFormation)
- Experience with containerization technologies (Docker, Kubernetes)
- Familiarity with CI/CD pipelines and tools (Jenkins, GitLab CI, GitHub Actions)
- Knowledge of scripting languages (Python, Bash, PowerShell)
- Understanding of networking concepts and security best practices
- Experience with monitoring and logging tools (e.g., Prometheus, ELK stack)
- Familiarity with version control systems (Git)
- Strong problem-solving and analytical skills
- Excellent communication and collaboration abilities"
- Basic understanding of cloud services and DevOps practices
- Experience in basic cloud infrastructure management
- Familiarity with containerization concepts
- Basic scripting skills for automation tasks"
- Proficiency in managing complex cloud environments
- Experience in implementing and managing Kubernetes clusters
- Ability to design and implement robust CI/CD pipelines
- Strong skills in Infrastructure as Code (IaC)
- Experience in implementing monitoring and alerting solutions"
- Expertise in cloud architecture design and implementation
- Strong skills in optimizing cloud costs and performance
- Ability to implement advanced DevOps practices (e.g., GitOps, ChatOps) and best practices
- Experience in managing multi-cloud or hybrid cloud environments
- Proficiency in implementing security best practices in cloud and DevOps processes"
- Demonstrated ability to lead large-scale cloud migration projects
- Strong skills in capacity planning and resource optimization
- Experience in mentoring junior DevOps engineers
- Ability to drive adoption of new technologies
- Proficiency in implementing disaster recovery and business continuity solutions" "Knowledge of serverless computing technologies
- Experience with configuration management tools (Ansible, Puppet, Chef)
- Familiarity with microservices architecture
- Understanding of machine learning operations (MLOps)
- Experience with cloud-native development practices"
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