V
Lead Software Engineer
V
- Posted 15 hours ago
- Be among the first 10 applicants
Job Description
Overview Performance Engineer to support performance, scalability, and reliability testing across enterprise applications and services. You'll help build and run load tests, analyze results using modern observability tooling, and partner with engineering teams to identify and resolve performance bottlenecks.
Preferred Thick Client Desktop Performance Testing Experience.
Nice to have exposure to AI agentic tooling/workflows.
Key Responsibilities:Create and execute performance test plans and workload models (baseline, load, stress, endurance).
Develop/maintain load test scripts using LoadRunner, JMeter, andor BlazeMeter.
Analyze test results and system behavior using Dynatrace, Splunk, Grafana (logsmetricstraces).
Support testing in Physical VSI as well as Cloud platforms like (AWS, Kubernetes environments) - autoscaling, resource limits, configuration impacts.
Contribute to automation and CICD integration for repeatable performance regression testing.
Document findings and communicate results and recommendations to QA/engineering partners.
Required Skills Experience 6 and 7+ years in performance testing, QA engineering (performance focus), SRE/observability, or similar.
Hands-on experience with LoadRunner and/or JMeter andor BlazeMeter.
Experience with performance/monitoring tools such as Dynatrace, Splunk, Grafana (or equivalent).
Scripting/programming Python, Java, Groovy, JavaScript
Understanding of performance concepts: latency, throughput, concurrency, bottlenecks, SLAs/SLOs.Preferred
Thick client/desktop application performance testing experience Nice to Have (AI / Agentic)Exposure to AI agentic workflows for engineering productivity (e.g., agent-assisted test generation, automated triage/correlation across logs-metrics-traces, runbook automation).
Create and execute performance test plans and workload models (baseline, load, stress, endurance).
Develop/maintain load test scripts using LoadRunner, JMeter, and/or BlazeMeter.
Analyze test results and system behavior using Dynatrace, Splunk, Grafana (logs/metrics/traces)
Support testing in Physical VSI as well as Cloud platforms like (AWS, Kubernetes environments) autoscaling, resource limits, configuration impacts
Preferred Thick Client Desktop Performance Testing Experience.
Nice to have exposure to AI agentic tooling/workflows.
Key Responsibilities:Create and execute performance test plans and workload models (baseline, load, stress, endurance).
Develop/maintain load test scripts using LoadRunner, JMeter, andor BlazeMeter.
Analyze test results and system behavior using Dynatrace, Splunk, Grafana (logsmetricstraces).
Support testing in Physical VSI as well as Cloud platforms like (AWS, Kubernetes environments) - autoscaling, resource limits, configuration impacts.
Contribute to automation and CICD integration for repeatable performance regression testing.
Document findings and communicate results and recommendations to QA/engineering partners.
Required Skills Experience 6 and 7+ years in performance testing, QA engineering (performance focus), SRE/observability, or similar.
Hands-on experience with LoadRunner and/or JMeter andor BlazeMeter.
Experience with performance/monitoring tools such as Dynatrace, Splunk, Grafana (or equivalent).
Scripting/programming Python, Java, Groovy, JavaScript
Understanding of performance concepts: latency, throughput, concurrency, bottlenecks, SLAs/SLOs.Preferred
Thick client/desktop application performance testing experience Nice to Have (AI / Agentic)Exposure to AI agentic workflows for engineering productivity (e.g., agent-assisted test generation, automated triage/correlation across logs-metrics-traces, runbook automation).
Create and execute performance test plans and workload models (baseline, load, stress, endurance).
Develop/maintain load test scripts using LoadRunner, JMeter, and/or BlazeMeter.
Analyze test results and system behavior using Dynatrace, Splunk, Grafana (logs/metrics/traces)
Support testing in Physical VSI as well as Cloud platforms like (AWS, Kubernetes environments) autoscaling, resource limits, configuration impacts
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