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Hybrid remote increases applicant pool, but senior niche performance SDET experience reduces overall competition.
Demands deep cloud-native, Kubernetes, observability, and data engine expertise, limiting cross-industry transferability.
Explicit 8+ years, staff-level performance SDET and strong Kubernetes, Go/Python/Java and observability requirements.
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Architect and own automation frameworks for performance and resilience testing of a highly distributed, multi-cluster control plane in the Anywhere Cloud platform.
Define and execute test strategies that validate Zero-Trust security, API-first contracts, and cross-cluster resource management under high load conditions.
Collaborate cross-functionally to integrate components like Kubernetes-based services into scalable automated performance test suites and mentor junior test engineers on distributed systems performance testing.
8+ years in Software in Test (SDET), Performance or Quality Engineering with experience as Staff/Lead Engineer building performance testing frameworks for distributed cloud-native platforms.
Expertise in performance engineering using custom load generators and tools like JMeter, Gatling, Locust, or k6 to validate throughput, latency, and concurrency over REST, gRPC, and database connections.
Deep hands-on knowledge of Kubernetes scaling, resource profiling (node/pod), Service Mesh overhead evaluation, and benchmarking storage IOPS (Ceph, S3).
Proficient in Go, Python, or Java for building performance test frameworks and custom Kubernetes operators.
Experienced leader at IC4 (Staff/Lead Engineer) level skilled in architecting scalable automation for distributed cloud-native environments.
Strong technical understanding of compute/streaming engine performance (Spark, Kafka, Flink) and Lakehouse scale limitations (Apache Iceberg).
Well-versed in observability and profiling tools integration (Prometheus, Grafana, OpenTelemetry) to monitor system performance and identify bottlenecks in soak and load tests.