Establish performance baselines for critical services, defining Service Level Indicators (SLIs), Service Level Objectives (SLOs), and measurable targets for latency, throughput, and error rates.
Develop realistic workload models based on production traffic patterns and actual customer behavior.
Define performance testing strategies that support scalability, reliability, and continuous improvement across the platform.
Conduct capacity planning to ensure systems can handle increasing traffic volumes and anticipated peak-demand events.
Performance Testing Framework Development:
Design, build, and maintain a shared performance testing framework that product engineering and SDET teams can use consistently.
Create production-like testing environments using Kubernetes and cloud infrastructure, including appropriate test data and configurations.
Evaluate emerging performance testing tools and technologies through proof-of-concept initiatives and recommend suitable solutions.
Establish reusable testing standards and best practices to improve consistency and efficiency across engineering teams.
Automated Testing and Continuous Integration:
Integrate automated performance regression testing into CI/CD pipelines to identify performance degradation before production releases.
Establish performance thresholds and automated pass/fail criteria to maintain service quality.
Develop performance trend reports and monitoring mechanisms to identify emerging issues and track improvements over time.
Execute load, stress, soak, and spike testing to evaluate system behavior under different operating conditions.
Performance Analysis and Optimization:
Investigate and identify performance bottlenecks across APIs, databases, message queues, and frontend applications.
Conduct root cause analysis using distributed tracing, metrics, logs, and profiling tools.
Collaborate with development, DevOps, and infrastructure teams to resolve performance issues and improve system efficiency.
Translate complex performance metrics into clear engineering recommendations and influence technical decisions across teams.
Evaluate distributed systems behavior, concurrency challenges, rate limiting, caching, and other factors affecting application performance.
Technical Leadership and Mentorship:
Take ownership of performance engineering initiatives and drive continuous improvements in testing methodologies.
Mentor junior engineers and support their technical development through knowledge sharing, guidance, and code reviews.
Contribute to capability-building initiatives and establish engineering practices that raise performance and reliability standards across the organization.
Work collaboratively with cross-functional teams to ensure performance considerations are integrated throughout the software development lifecycle.
Requirements
At least 5 years of professional experience in performance testing, scalability engineering, reliability engineering, or a closely related discipline.
Demonstrated experience identifying, investigating, and resolving performance bottlenecks in production-scale applications and distributed systems.
Hands-on expertise with at least one modern performance testing tool, such as k6, Gatling, or Locust .
Strong programming skills in at least one language, preferably Python, JavaScript/TypeScript, or Java.
Experience designing realistic workload models and implementing distributed load generation for large-scale applications.
Practical experience with observability and monitoring platforms such as New Relic, Prometheus, Grafana, or OpenTelemetry.
Strong understanding of root cause analysis across API, database, and message queue layers using metrics, logs, distributed traces, and profiling techniques.
Solid knowledge of REST API performance, distributed systems, microservices architecture, concurrency, caching, and common application performance bottlenecks.
Experience integrating automated performance tests into CI/CD pipelines, including defining measurable performance thresholds and pass/fail criteria.
Working knowledge of Kubernetes and hands-on experience with at least one major cloud platform, such as Google Cloud Platform (GCP), AWS, or Microsoft Azure.
Ability to deploy, configure, troubleshoot, and maintain performance testing environments that closely reflect production infrastructure.
Strong analytical and problem-solving skills, with the ability to interpret performance results and turn findings into practical engineering recommendations.
Excellent collaboration and communication skills, enabling effective partnership with software developers, DevOps engineers, infrastructure teams, and other stakeholders.
Ability to take technical ownership, work independently, manage priorities, and contribute effectively in a fast-paced engineering environment.
Experience mentoring junior engineers and promoting engineering best practices is highly valued.
Nice-to-have qualifications
Experience with runtime profiling and thread or heap analysis for JVM or Node.js applications, including garbage collection behavior and event-loop latency.
Knowledge of frontend performance optimization, including Core Web Vitals, Lighthouse, and Real User Monitoring (RUM).
Experience using AI-assisted tools to improve test design, automate performance analysis, or interpret testing results.
Familiarity with NoSQL databases, search technologies, and message brokers operating at scale.
Experience with chaos engineering, resilience testing, and safe production testing techniques, including canary releases and shadow traffic.
Benefits
Fully remote working arrangement from India.
Full-time opportunity within a globally distributed engineering organization.
Opportunity to work on a large-scale, cloud-based platform supporting millions of businesses worldwide.
Exposure to modern technologies, distributed systems, microservices, Kubernetes, and cloud infrastructure.
Opportunity to lead performance engineering initiatives and influence technical standards across multiple engineering teams.
Professional development through hands-on technical challenges and continuous learning.
Opportunity to mentor engineers and contribute to the development of engineering capabilities.
Collaborative, remote-first environment that encourages ownership, innovation, and technical excellence.
How Jobgether works
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether?
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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