Preferred Tools/Technologies

- JMeter

- Groovy / JavaScript

- Datadog

- AppDynamics

- Kibana / Elasticsearch

- AWS monitoring

- Docker / Kubernetes / ECS / EKS

- CI/CD pipeline integration

- Git / Jenkins

- API testing tools

- SQL/log analysis

8+ years Over all Experience with 5+ Years of Experience in Performance Testing

1. Performance Test Scripting

- Strong experience with JMeter scripting.

- Ability to write/debug custom scripts using Groovy and/or JavaScript.

- Experience with correlation, parameterization, assertions, timers, controllers, reusable functions, and custom logic.

- Ability to troubleshoot script failures, dynamic values, authentication, cookies, headers, and session handling.

2. Workload Modeling and Test Design

- Ability to analyze business flows and design realistic workload models.

- Experience designing load, stress, endurance, spike, and scalability tests.

- Ability to calculate user load, TPS/TPM, pacing, think time, ramp-up, and transaction mix based on production usage.

- Experience validating test data readiness and environment readiness.

3. Production Log and Traffic Analysis

- Ability to analyze production logs and traffic patterns to identify peak usage, transaction volume,

error trends, and response-time behavior.

- Experience using production metrics to create accurate performance test scenarios.

- Ability to compare production behavior with test environment results.

4. Monitoring and Observability

- Hands-on experience with Datadog, AppDynamics, and Kibana.

- Ability to monitor application, API, JVM/.NET, IIS, database, container, and infrastructure metrics.

- Experience identifying bottlenecks related to CPU, memory, GC, thread pools, connection pools,

database queries, downstream services, cache, and network latency.

- Ability to correlate JMeter results with monitoring tool metrics.

5. AWS / Cloud / Container Monitoring

- Experience monitoring applications hosted on AWS cloud platforms.

- Understanding of containers, Kubernetes/ECS/EKS, pods, services, nodes, CPU/memory limits,

autoscaling, and cloud infrastructure behavior.

- Ability to analyze container-level performance issues and work with DevOps teams.