Senior Software Development Engineer in Test

•     Lead the design and implementation of chaos, failure, and performance test frameworks targeting distributed backend systems and infrastructure.

•     Build and maintain CI/CD pipelines in Jenkins using Groovy-based DSL and scripted/declarative pipeline definitions.

•     Own infrastructure-level test environments using Kubernetes — spinning up, tearing down, and managing test clusters for reliability experiments.

•     Execute and automate chaos engineering scenarios: network partitions, node failures, pod evictions, resource exhaustion, and latency injection.

•     Design and run performance and load tests to identify bottlenecks, regressions, and capacity limits across services.

•     Develop failure testing strategies to validate system behavior under degraded conditions — partial failures, cascading failures, and data corruption scenarios.

•     Define quality metrics and SLOs for infrastructure reliability; report on test coverage and failure patterns to engineering leadership.

•     Mentor and guide junior QA engineers; champion a reliability-first quality culture across the engineering org.

•     Strong programming skills in Python for test automation, tooling, and scripting.

•     Proficiency in Groovy, particularly for Jenkins pipeline development (scripted and declarative).

•     Extensive hands-on Jenkins experience — pipeline authoring, plugin management, shared libraries, and CI/CD architecture.

•     Deep Kubernetes knowledge — deploying workloads, managing namespaces, configuring resource limits, and operating clusters for test environments.

•     Proven experience in chaos engineering: fault injection, resilience testing, and tools such as Chaos Monkey, Litmus, or Gremlin.

•     Hands-on performance testing experience: load generation, profiling, bottleneck analysis, and tooling (e.g., Locust, k6, Gatling, JMeter).

•     Experience designing failure test scenarios for distributed systems — covering partial outages, dependency failures, and data-path degradation.

•     Strong understanding of Linux, networking, and infrastructure fundamentals.

•     Experience with service meshes (Istio, Linkerd) for traffic shaping and fault injection.

•     Familiarity with observability tooling: Prometheus, Grafana, Jaeger, or OpenTelemetry for validating test outcomes.

•     Background in SRE practices: SLOs, error budgets, incident post-mortems.

•     Experience with cloud infrastructure testing on AWS, GCP, or Azure at the platform level.

•     Exposure to eBPF-based tools for low-level system observability during failure tests.