Veeam is the Data and AI Trust Company, specializing in helping organizations ensure their data and AI are fully understood, secured, and resilient to enable the acceleration of safe AI at scale. As the market leader in both data resilience and data security posture management, Veeam is built for the convergence of identity, data, security, and AI risk. Headquartered in Seattle with offices in more than 30 countries, Veeam protects over 550,000 customers worldwide, who trust Veeam to keep their businesses running. Join us as we go fearlessly forward together, growing, learning, and making a real impact for some of the world’s biggest brands.

About the Role

Veeam Data Cloud provides cloud-based data protection. Our Site Reliability Engineering team uses software, telemetry, and systems engineering to improve service reliability and make product changes safer to deliver.

As a Senior Software Engineer, Reliability, you will own specific services, tools, or components from design through production operation. You will use AI coding agents as part of the development workflow and apply your engineering judgment to the design, correctness, and reliability of the result.

The role combines planned software development with troubleshooting, maintenance, and incident response. You will work with Staff engineers and product teams to turn operational problems into lasting improvements in software and systems.

What You’ll Do

Build and ship production-ready reliability software using AI coding agents. Break down problems, define requirements and acceptance criteria, guide implementation, review generated code, and validate changes through comprehensive testing
Develop reusable libraries, services, controllers, Kubernetes components, infrastructure modules, and service templates for deployment checks, rate limiting, circuit breaking, and load management
Build observability capabilities, including telemetry pipelines for metrics, logs, and traces, as well as SLIs, SLOs, and error-budget tooling that supports release decisions.
Design and implement safe-release capabilities, including canary deployments, automated checks, rollback mechanisms, and delivery-pipeline integrations
Create fault-injection, failure-recovery, and load tests to identify resilience and performance issues before they affect production
Design and improve distributed services, initially on Azure, with a focus on failure modes, graceful degradation, recovery, API quality, and production readiness
Use metrics, logs, and traces to diagnose issues, validate releases, and keep alerts relevant and actionable
Respond to and lead incidents during assigned coverage. Capture incident context, document findings, track corrective actions, and implement improvements that prevent recurrence
Review code and technical designs, document significant decisions and shared components, and help colleagues adopt and maintain the systems you own

What You’ll Bring

4+ years of software engineering experience for cloud-based products, including delivering changes to production and diagnosing problems in running services
Practical experience delivering software with AI coding agents: breaking down work, providing context and constraints, guiding implementation, reviewing changes, and verifying behavior through tests and production telemetry
Ability to explain, debug, and modify production code in at least one backend language, such as Go, TypeScript/Node.js, C#, or Java. You must be able to recognize incorrect or incomplete generated code and take responsibility for the final implementation
Experience designing or operating distributed systems, with an understanding of timeouts, retries, dependency failures, and recovery behavior
Hands-on experience with Kubernetes, infrastructure as code using Terraform, and automated build, test, and deployment pipelines. Experience with one CI/CD platform is sufficient; examples include GitHub Actions, GitLab CI, and Argo CD
Experience using metrics, logs, and traces to investigate service behavior and using SLOs or error budgets to guide engineering decisions