Senior Software Development Engineer in Test; SDET

Overview Production Engineering is seeking a Sr, Software Development Engineer in Test (SDET) to drive system reliability, scalability, and performance validation through automation and engineering rigor. This role goes beyond traditional testing—you will build systems and frameworks that validate production readiness, enforce performance and reliability standards, and ensure services behave correctly under real-world conditions. You will operate at the intersection of software engineering, performance engineering, and system reliability, driving the strategy, architecture, and implementation of validation frameworks that ensure services are reliable, scalable, and production-ready.

• Build and maintain automated validation frameworks for APIs, distributed systems, and critical service workflows.
• Develop integration and end-to-end validation suites that reflect real production use cases.
• Ensure validation is fully integrated into CI/CD pipelines with meaningful quality gates.
• Design systems and tooling that improve testability, observability, and diagnosability.
• Drive improvements in validation coverage and overall system confidence through data‑driven analysis and risk‑based prioritization.
• Drive reduction of flaky or low‑signal tests to ensure high signal‑to‑noise automation.

• Design and implement load, stress, and endurance testing strategies for distributed systems.
• Build and maintain performance test frameworks and workloads that model real traffic patterns.
• Define and maintain performance baselines, SLAs/SLOs, and capacity models that guide engineering and operational decisions.
• Analyze system behavior under load to identify bottlenecks, resource contention, and scaling limits.
• Drive performance improvements by working directly in code, configuration, and infrastructure.
• Integrate continuous performance validation into CI/CD and pre‑production workflows.

• Ensure services meet production readiness standards, including reliability, scalability, and observability.
• Improve system confidence by validating real‑world scenarios, edge cases, and degradation paths.
• Advocate for engineering practices that improve operability and system robustness.
• Drive reliability validation efforts for complex distributed systems, including resilience, failover, and recovery testing.
• Influence system architecture by advocating for reliability, observability, and operability best practices.
• Use production telemetry and incident learnings to continuously improve validation and performance strategies.

• Bachelor’s degree in Computer Science, Engineering, or related field.
• 6–8 years of experience in software engineering, SDET, SRE or related roles.
• Strong programming skills (Java, Python, C#, or JavaScript) with ability to build production-quality code.
• Proven experience designing and implementing scalable automation frameworks or validation platforms.
• Hands‑on experience with API and distributed systems testing.
• Experience with performance testing tools (e.g., k6, JMeter, Gatling).
• Familiarity with CI/CD systems and modern development workflows.
• Solid understanding of system design, scalability, and performance fundamentals.

• Experience in Production Engineering, SRE, or infrastructure-focused teams.
• Familiarity with microservices and cloud-native architectures.
• Experience with observability tools (e.g., metrics, logs, tracing—Grafana, Prometheus, Datadog).
• Knowledge of containers and orchestration (Docker, Kubernetes).
• Exposure to resilience testing, chaos engineering, or fault injection.
• Experience modeling production traffic patterns or capacity planning.

• Strong systems-thinking mindset with deep understanding of distributed system behavior under load and failure conditions.
• Ability to lead complex technical initiatives with minimal direction.
• Strong technical judgment and ability to balance reliability, performance, and engineering velocity.
• Experience influencing engineering teams through technical leadership and collaboration.
• Exceptional troubleshooting and root‑cause analysis skills.

• Systems are validated against realistic production scenarios, not just functional correctness.
• Improved system reliability through proactive validation of edge cases and failure modes.
• Strong contribution to a culture of engineering ownership of quality and performance.
• Performance and scalability risks are identified and mitigated early in the development lifecycle.
• Validation frameworks become trusted engineering tools used across teams.
• Reliability, performance, and production-readiness standards are consistently met across supported services.
• Engineering teams proactively leverage validation and performance insights to improve system design.