The Performance Testing Engineer will validate application, API, integration and data-platform performance under expected and exceptional workloads. The role will design and execute load, stress, endurance, spike and capacity tests; identify system bottlenecks; correlate application and infrastructure monitoring data; and provide actionable recommendations before production releases.

Must Have Technical/Functional Skills

- Strong understanding of:
- Load testing
- Stress testing
- Spike testing
- Endurance or soak testing
- Volume testing
- Scalability testing
- Capacity validation
- Failover and resilience testing
- Ability to translate business volumes and non-functional requirements into realistic test workloads.

Performance-test tools Robust hands-on experience with at least one of:
- Apache JMeter
- Micro Focus LoadRunner or OpenText LoadRunner
- Gatling
- NeoLoad
- k6

Preferred exposure to more than one tool.

Test scripting

- Experience creating reusable scripts for:
- Strong knowledge of:
- Correlation
- Parameterization
- Dynamic data handling
- Session management
- Token handling
- Ability to create and manage test data for large-scale execution.

Workload and capacity modeling
- Experience analyzing:
- Concurrent users
- Transactions per second
- Hourly and daily business volumes
- Peak-period behavior
- Batch windows
- Message or event rates
- Data volumes
- Ability to create workload models based on production usage, business growth assumptions and release scope.
- Capacity-planning and infrastructure-sizing awareness.

Monitoring and bottleneck analysis Hands-on experience with monitoring or observability tools such as:
- Dynatrace
- AppDynamics
- Datadog




- Splunk
- AWS CloudWatch
- Grafana
- Prometheus
- New Relic

Ability to correlate test results with:
- CPU
- Memory
- Disk and network utilization
- JVM and garbage collection
- Thread and connection pools
- Database waits and locks
- Query response times
- API latency
- Container and Kubernetes metrics
- Queue depth and consumer lag
- Application errors and timeouts

Result analysis
- Strong knowledge of:
- Response-time percentiles, particularly p90, p95 and p99
- Throughput
- Transactions per second
- Error percentage
- Concurrent users
- Resource saturation
- Apdex or comparable service-quality measures

CI/CD and engineering integration

Monitoring and technology keywords

Dynatrace, AppDynamics, Datadog, Splunk, CloudWatch, Grafana, Prometheus, New Relic, JVM, garbage collection, database tuning, PostgreSQL, Amazon RDS, AWS, Kubernetes, microservices, REST API, SOAP, Kafka, Salesforce, Databricks

Pipeline and release keywords

GitLab CI/CD, Jenkins, Azure DevOps, GitHub Actions, performance quality gate, release readiness, baseline testing, regression performance testing, production monitoring

Suggested Boolean search string

("Performance Test Engineer" OR "Performance Engineer") AND (JMeter OR LoadRunner OR NeoLoad OR Gatling OR k6) AND ("load testing" OR "stress testing") AND (Dynatrace OR AppDynamics OR CloudWatch OR Grafana)

Essential Skills: Performance testing| load/stress testing| bottleneck analysis| monitoring| capacity validation| test scripting| result analysis and release performance readiness.

Skills: Performance Testing using NeoLoad~Performance Testing

Experience Required: 8-10

Location: Hyderabad

📌 Performance Test Engineer (Chennai)
🏢 Orcapod Consulting Services
📍 Chennai