Performance Test lead

Here Technologies
McLean, United States of America
yesterday

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

McLean, United States of America

Tech stack

Java
JavaScript
Amazon Web Services (AWS)
Confluence
JIRA
Azure
Cloud Computing
Computer Programming
Continuous Integration
Database Connection
Github
Groovy
HP Loadrunner
Java Database Connectivity
JMeter
Python
Load Testing
OAuth
Prometheus
Security Assertion Markup Language (SAML)
Simple Object Access Protocol (SOAP)
Test Data
WebSocket
Data Processing
Scripting (Bash/Python/Go/Ruby)
Google Cloud Platform
Performance Testing
Neoload
Grafana
Gatling
GIT
Containerization
Gitlab-ci
Kubernetes
REST
gRPC
New Relic (SaaS)
Appdynamics
Software Version Control
Dynatrace
Docker
ELK
Jenkins

Requirements

Typical experience: 10+ years in performance testing/performance engineering, with 2+ years in a lead or senior contributor role. Demonstrable experience designing and executing large-scale performance tests for production-like environments. Proven track record of identifying and resolving performance bottlenecks and influencing architecture/design decisions.

Required technical skills & tools Performance test tools: expertise with one or more of JMeter, LoadRunner (VuGen/Controller), k6, Gatling, Locust, NeoLoad. Scripting/programming: strong scripting skills in Java, Python, JavaScript, or Groovy for test scripting and automation. Protocols and technologies: HTTP/HTTPS, WebSockets, REST, SOAP, gRPC, database connectivity (JDBC), and authentication flows (OAuth, SAML). CI/CD & automation: Jenkins/GitHub Actions/GitLab CI integration for scheduled and pipeline-triggered tests. Cloud & infrastructure: hands-on exposure to AWS/Azure/Google Cloud Platform for distributed load generation, containerization (Docker), and Kubernetes. Monitoring & APM: experience with Grafana, Prometheus, New Relic, Dynatrace, AppDynamics, or ELK stack for metrics and traces. Data handling: ability to design realistic test data, parameterization, and correlation for accurate workload simulation. Version control & collaboration: Git, Jira, Confluence, or equivalent.

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