DevOps Software Engineer (Top Secret with agreement to obtain CI Poly)

***Active TS clearance required; must be eligible for CI Polygraph. Applicants without an active TS clearance will not be considered.. *** North Point Technology is looking to hire a DevOps Software Engineer to support the Erebus Program, delivering next-generation geospatial intelligence and data management applications to the National Geospatial-Intelligence Agency (NGA). The qualified candidate must have an active Top Secret security clearance to start, with the ability to obtain and maintain a higher-level clearance with polygraph. Join a premier geospatial intelligence program supporting over 14,000 analysts and decision-makers worldwide, helping pioneer automated, machine-generated geospatial analysis capabilities that will transform how intelligence reaches stakeholders. Responsibilties: This position involves designing, building, and delivering enterprise-grade software for the world's premier geospatial intelligence program. Duties include developing multi-tiered and microservice applications, deploying containerized workloads to cloud environments, building and maintaining CI/CD pipelines, scripting and automation, working in Linux environments, and collaborating with Agile teams to deliver innovative capabilities that directly support critical national security missions. Required Skills/Experience: Object-oriented programming (OOP) experience building enterprise-level applications (e.g., Java, C#) Experience developing multi-tiered or microservice applications Experience with a major cloud platform (AWS, Azure, or Google Cloud) Containerization experience (Docker, Kubernetes, or OpenShift) Experience with CI/CD tools (GitLab CI, Jenkins) Scripting experience (e.g., Python, Bash/Shell) Preferred Skills/Experience: Experience supporting US government agencies Experience using Amazon Web Services (AWS) Infrastructure as Code (IaC) experience (Ansible, Puppet, Terraform, or CloudFormation) Experience deploying and maintaining Kubernetes distributions Familiar