Sr DevOPS Engineer (Snowflake, DBT and Qlik)

Job Type:- Fulltime Position:- Sr DevOPS Engineer (Snowflake, DBT and Qlik) Location:- Remote/Hybrid- only from Raleigh, NC | Phoenix, AZ | Dallas, TX Job description:- Sr 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 warehouses CORE 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 • bility to balance speed of delivery with stability and safety