Senior Performance Engineer
Role Summary
We are seeking a Senior Performance Engineer to serve as a technical leader responsible for ensuring large-scale, mission?critical enterprise applications meet strict performance, scalability, resiliency, and efficiency standards.
This role focuses on cloud?native, distributed systems and requires deep expertise in performance engineering, observability, automation, and advanced analytics. You will lead performance strategy end to end—shaping non?functional requirements, designing realistic performance tests, diagnosing complex system behavior, and influencing architectural decisions to deliver highly performant systems across hybrid and cloud environments.
This is a senior individual contributor role with strong cross?functional influence and technical leadership responsibilities.
Key Responsibilities
Performance Engineering Leadership
- Own end?to?end performance engineering activities from workload modeling through analysis and optimization.
- Define and validate performance KPIs, SLAs, SLOs, and non?functional requirements.
- Act as a performance SME, mentoring engineers and promoting best practices across teams.
Cloud?Native & Distributed Systems Performance
- Design and execute performance tests for microservices?based, containerized, and cloud?hosted systems (AWS, Azure, Google Cloud Platform).
- Analyze and optimize autoscaling behavior, load balancing, APIs, and distributed workloads.
- Evaluate cost?performance tradeoffs across compute, storage, and networking resources.
- Leverage native cloud monitoring and telemetry platforms to assess system behavior.
Performance Testing & Automation
- Build and maintain performance test scripts using tools such as LoadRunner, JMeter, Gatling, Locust, or K6.
- Integrate performance testing into CI/CD pipelines using Jenkins, GitHub Actions, or Azure DevOps.
- Create reusable, modular workloads that reflect real?world traffic and usage patterns.
Monitoring, Observability & Optimization
- Use APM and observability tools (e.g., Dynatrace, Datadog, AppDynamics, New Relic, Grafana) for real?time monitoring.
- Define observability standards including metrics, structured logging, and distributed tracing.
- Perform deep performance diagnostics across application, database, messaging, caching, and integration layers.
- Recommend tuning and optimization strategies for APIs, microservices, databases (SQL/NoSQL), JVM/.NET, and cloud infrastructure.
- Evaluate resilience and scalability via failover, chaos testing, and peak readiness assessments.
Advanced Analytics & AI?Driven Optimization
- Apply AI/ML?based techniques to detect anomalies, identify bottlenecks, and predict performance issues.
- Use intelligent analytics to accelerate root?cause analysis and capacity planning.
- Incorporate insights into dashboards and performance reporting.
Collaboration & SDLC Integration
- Partner closely with Engineering, Architecture, DevOps, SRE, QA, and Product teams.
- Ensure performance considerations are embedded early in the SDLC through design and architecture reviews.
- Produce clear performance documentation including baselines, reports, RCA summaries, and capacity models.
Qualifications
- Expert knowledge of performance engineering, distributed systems, concurrency, and cloud?native architectures.
- Proven ability to analyze complex performance data and influence technical and architectural decisions.
- Strong hands?on experience with cloud platforms (AWS, Azure, or Google Cloud Platform).
- Experience with performance testing frameworks and observability/APM tools.
- Familiarity with CI/CD integration for performance testing.
- Experience with AI/ML?driven observability or predictive analytics is a strong plus.
- Domain experience in highly regulated or transaction?heavy environments is advantageous.
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