Software Development Engineer in Test

• Kubernetes-based distributed systems at scale

• Observability and alerting pipelines

• AI-assisted incident investigation systems

• Multi-cloud (AWS, Azure, GCP) environments, Networking Deployments

• Reliability validation, chaos testing, and failure injection systems

• Infrastructure and deployment automation pipelines

Key Responsibilities

• Define and own the strategy for system-level validation

• Design and build scalable automation frameworks for:

• API, integration, and end-to-end testing

• Kubernetes and system-level validation

• Regression and reliability pipelines

• Build systems that proactively detect failures before they reach production

• Drive chaos engineering and failure injection practices

• Establish CI/CD reliability gates with strong validation coverage

• Partner with SRE, platform, and backend teams to ensure systems are both observable and testable

• Lead incident analysis with a focus on improving validation and preventing recurrence

• Mentor engineers and raise the bar for system reliability and quality

Technical Expectations

Deep Expertise In

• Kubernetes internals, debugging, and multi-cluster systems

• Distributed systems behavior and failure modes

• Observability stacks and alerting frameworks

• Production incident handling and root cause analysis

Strong Hands-on Experience With

• EKS, GKE, or managed Kubernetes platforms

• Networking concepts: VPC, load balancers, service communication, IAM

• Chaos testing and reliability engineering practices

• Designing large-scale automation and validation systems

Programming

• Strong coding skills in Python and Go (mandatory)

• Experience building automation frameworks and system-level tooling

• Proficiency in Shell scripting and infrastructure automation

What Makes This Role Different

• You are responsible for ensuring systems are provably reliable, not just operational

• Deep QA and validation engineering

• Focus on testing distributed systems, not just application features

• Work on failure scenarios, not just happy paths

What Success Looks Like

• A robust validation layer that continuously tests system reliability

• Significant reduction in production incidents and faster recovery times

• Strong alignment between observability signals and real system behavior

• Clear ownership of both reliability and quality across the platform

Scope

• Own platform-wide reliability and validation architecture

• Drive cross-team initiatives across SRE, platform, and engineering

  • Act as a technical leader in reliability, automation, and system validation