DevOps Engineer

Sunwater Capital•North Bethesda, MD

10d•Onsite

About The Position

Reframe Data Services is seeking a DevOps Engineer to support our development teams and the cloud environments their applications run in. You will focus on getting applications onto our platform, building and maintaining CI/CD pipelines, and making it easier for engineers to move their work from a pull request to production safely. This is a developer-facing role. You'll work closely with application teams including engineers based outside the US to understand how they build, then provide the automation, tooling, and documentation that helps them move faster without sacrificing reliability. You will also share responsibility for the health of our production systems, participating in an on-call rotation, responding to incidents, and doing the follow-up work that prevents the same problem from happening twice.

Requirements

Experience using infrastructure as code like Terraform, OpenTofu, or CloudFormation, including maintaining code that other engineers depend on
Experience building and maintaining CI/CD pipelines using tools such as GitHub Actions, GitLab CI, AWS CodeBuild and CodePipeline, or Jenkins
Experience with containers and container orchestration tools like Docker, ECS, and/or EKS
Solid Linux administration and troubleshooting skills
Direct experience with cloud technologies such as Amazon Web Services (AWS), including compute, networking, storage, and IAM
Scripting or programming ability in at least one language (Python, Bash, Go, or similar) sufficient to automate real work
Ability to work on-site at a state-of-the-art building in North Bethesda, MD office.
3+ years in a DevOps, site reliability, cloud engineering, or comparable role supporting production systems.
Comfort with Git-based workflows and branching strategies on a team.
Experience taking part in on-call rotations, incident response, and post-incident review.
Strong written communication. This role produces documentation and standards that other engineers rely on.

Nice To Haves

Practical experience using AI coding assistants such as Claude to automate day-to-day engineering work: generating and reviewing infrastructure code, drafting pipeline configuration, producing runbooks and documentation, and building small internal tools.
Experience supporting teams distributed across multiple time zones.
Kubernetes experience, particularly EKS.
Familiarity with monitoring and alerting tools (CloudWatch, Prometheus, Grafana, Datadog, OpenTelemetry) and with writing alerts that are useful rather than noisy.
Configuration management experience with Ansible, Puppet, or Chef.
Exposure to regulated environments (HIPAA, SOC 2, FedRAMP, or similar).
Experience with data-heavy workloads, ETL pipelines, or analytics applications.

Responsibilities

Onboard applications. Bring new and existing applications onto our cloud environment, establishing repeatable patterns for build, deployment, configuration, secrets management, and monitoring.
Build and maintain CI/CD pipelines. Design pipelines that give teams fast, reliable feedback, including automated testing, artifact management, promotion between environments, and safe release processes.
Improve developer experience. Find out what slows development teams down and fix it with better tooling, self-service options, sensible defaults, and clear documentation.
Build and maintain infrastructure. Provision cloud resources using infrastructure as code, and keep environments current through routine patching, upgrades, and dependency updates.
Support production systems. Participate in an on-call rotation, diagnose and resolve incidents across both the application and infrastructure layers, and identify root causes so fixes hold rather than needing to be reapplied.
Work alongside engineering teams. Contribute to architecture discussions, design reviews, and code review, with an eye toward how easily a change can be deployed, tested, and supported.
Improve our AWS environment. Evaluate our accounts and workloads for opportunities in security, availability, performance, and cost, and see those improvements through to completion.
Solve problems with code. Turn loosely defined requirements into working solutions, writing scripts or small tools when an existing product doesn't fit the need, and using AI coding assistants such as Claude to work through that kind of task quickly.