Job Description

Who We’re Looking For (Position Overview):

Spry Methods is on the search for a DevOps Engineer to join our team in Herndon, VA. You will work in a highly critical production environment where your expertise in automation and systems engineering is essential. Supporting all three leading hyperscalers - AWS, Azure, and GCP, you will have a unique opportunity to develop and maintain solutions across multiple cloud platforms.

In this role, you will leverage infrastructure-as-code, CI/CD pipelines, and container orchestration tools to ensure reliable, scalable, and secure cloud operations. You will also develop and maintain scripts and automation tools to streamline deployment, configuration management, and system maintenance.

We emphasize cloud-agnostic automation using tools like Terraform to build reusable infrastructure that works consistently across AWS, Azure, and GCP by leveraging common features across cloud providers. This role involves implementing efficient engineering and operational processes, as well as establishing best practices to continuously enhance operational efficiency within and across teams.

Our team values engineers who take full responsibility for their work. You demonstrate end-to-end ownership of systems and tasks. Proactive and accountable, you take pride in maintaining and continuously improving your work in production to ensure it delivers long-term value.

What Your Day-To-Day Looks Like (Position Responsibilities):

• Develop and maintain reusable Terraform scripts, modify and run infrastructure code from existing modules, and troubleshoot deployment issues across AWS, Azure, and GCP environments.
• Develop new Ansible playbooks, and modify, troubleshoot, and maintain existing Ansible code to automate infrastructure configuration and management tasks.
• Configure, deploy, and manage Kubernetes clusters including networking, storage, security, service mesh, and load balancer configuration; use tools like Helm for deployment, and monitor and troubleshoot cluster performance.
• Design, implement, and maintain CI/CD pipelines using tools like GitLab CI and ArgoCD; write and optimize pipeline scripts for automated testing and deployment; leverage GitOps practices for managing infrastructure and application delivery.
• Cloud Platforms : Strong hands-on experience with public cloud platforms (AWS, Azure, GCP), including core services such as virtual networking, compute, storage, IAM, serverless functions, and monitoring.
• Terraform : Deep hands-on experience developing, maintaining, and troubleshooting infrastructure-as-code using Terraform across multi-cloud environments.
• Ansible : Experience using Ansible to automate day-to-day operational tasks, including configuration management and troubleshooting of infrastructure resources.
• CI/CD & GitOps : Hands-on experience with GitLab CI and ArgoCD to design and maintain pipelines for automated testing, deployment, and infrastructure management using GitOps principles.
• Linux & Automation : Proficient in Linux system administration and automation using Python to streamline infrastructure management and operational tasks.
• Serverless Computing : Experience developing, deploying, and managing serverless functions using cloud-native services such as AWS Lambda, Azure Functions, and Google Cloud Functions.

What You Need to Succeed (Minimum Requirements)

• U.S. Citizenship
• Bachelor’s degree in computer science, Engineering, or equivalent
• 8+ years of experience in cloud infrastructure, DevOps, or related engineering roles.
• Deep hands-on expertise with Terraform, Ansible, Kubernetes, GitLab CI, and ArgoCD in production environments.
• Strong understanding of cloud platforms (AWS, Azure, GCP) and their core services (compute, storage, networking, IAM, serverless functions).
• Experience developing and maintaining CI/CD pipelines and automating deployments using GitOps practices.
• Proficient in Linux system administration and infrastructure automation using Python.

Ideally, you'll also have (Preferred Experience)

• Experience deploying and managing Kafka on Kubernetes, including performance tuning, scaling, and monitoring in a production environment

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