We are seeking an experienced Performance Engineering Specialist Lead Engineer with strong expertise in application performance engineering, system scalability, and load testing. This role involves designing and executing performance strategies, identifying performance bottlenecks, optimizing system behavior, and collaborating with cross-functional teams to ensure high-performing and scalable applications. The ideal candidate should have a deep understanding of performance tools, distributed architectures, cloud environments, and modern application frameworks.
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Lead performance engineering strategy, planning, and execution across software development lifecycle.
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Conduct performance benchmarking, load testing, stress testing, endurance testing, and scalability testing.
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Analyze system performance metrics and identify performance bottlenecks in applications, APIs, databases, infrastructure, and networks.
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Work closely with architects, developers, DevOps, and product teams to optimize performance issues and recommend solutions.
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Develop performance test scripts and frameworks using tools such as JMeter, LoadRunner, Gatling, or similar.
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Implement performance monitoring and observability best practices using platforms like Dynatrace, AppDynamics, Grafana, New Relic, or Splunk.
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Build automated performance test pipelines integrated into CI/CD environments.
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Evaluate system architecture and recommend optimizations related to cloud infrastructure, caching, memory management, database tuning, and concurrency.
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Prepare detailed performance reports with actionable recommendations and communicate findings to leadership and technical stakeholders.
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Drive capacity planning, scalability modeling, and production readiness validation.
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Mentor and guide engineering teams on performance best practices.
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Minimum 12+ years of experience in performance engineering, performance testing, and system optimization.
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Strong hands-on experience with performance testing tools such as JMeter, LoadRunner, Gatling, or k6.
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Experience with APM and monitoring tools including Dynatrace, AppDynamics, New Relic, Datadog, Splunk, or similar.
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Solid understanding of microservices architecture, distributed systems, cloud platforms (AWS, Azure, or GCP), and Kubernetes.
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Strong programming/scripting knowledge in Java, Python, Shell, or similar technologies.
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Proficiency in performance analysis of web applications, APIs, databases (SQL/NoSQL), and infrastructure.
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Experience integrating performance testing into automated CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI).
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Strong understanding of protocols such as HTTP, REST, gRPC, messaging queues, and caching technologies.
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Ability to interpret profiling data, thread dumps, heap dumps, and analyze GC behavior.
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Excellent analytical, problem-solving, interpersonal, and communication skills.