Sr DevOPS Engineer
ARCHIVED
We can't find an active application page for this role right now. It may reopen or be listed elsewhere. Use Next Steps to search for an active apply link and similar live jobs.
Job Title: Sr DevOPS EngineerLocation - San Jose, CAFTE OnlyJob DescriptionSr DevOPS Engineer (Snowflake, DBT and Qlik)• CI/CD tools: Azure DevOps Pipelines or GitLab CI/CD (hands-on pipeline development)• Infrastructure as Code: Terraform (AWS and Azure providers) - production-grade experience• Configuration Management: Ansible and/or Puppet - ability to write playbooks/manifests and manage infrastructure state• Cloud platforms: AWS (EC2, S3, RDS, VPC, IAM, Lambda, Glue, Lakeformation) and Azure (VMs, App Services, Blob Storage, Cosmos DB, networking)• Python programming: scripting, automation, API integration, and tooling development• Snowflake: operational knowledge of warehouse management, cost optimization, and cloud integration• Git/GitLab/GitHub: version control, branching strategies, and repository management• Linux/Unix system administration and command-line proficiency• Networking fundamentals: VPCs, subnets, security groups, DNS, load balancing• Scripting languages: Bash, Python, or similar for automation• 5+ years in DevOps, Platform Engineering, or Infrastructure Engineering• 3+ years hands-on with Terraform and Infrastructure as Code• 3+ years with CI/CD tools (Jenkins, GitLab CI, Azure DevOps, or similar)• 2+ years with configuration management tools (Ansible, Puppet, or similar)• 2+ years supporting cloud platforms (AWS and/or Azure in production)• 1+ years with Python automation and scripting• Experience supporting or integrating with Snowflake or modern data warehousesCORE COMPETENCIES:• Strong automation mindset: identify and eliminate manual toil• Systems thinking: understand full deployment pipelines and infrastructure dependencies• Problem-solving and troubleshooting skills• Clear communication with both technical and non-technical stakeholders• Detail-oriented with focus on reliability and repeatability• Comfortable with continuous learning of new tools and cloud services• Collaborative approach to working with data engineering teams