Performance Engineer

The Senior Performance Engineer is a technical leader responsible for ensuring that large-scale, mission-critical applications-particularly mortgage processing, document automation, BPM workflows, and rules engines-meet stringent performance, scalability, and resiliency goals.

This role requires deep expertise in performance engineering, distributed systems, cloud-native architectures, and AI-driven analysis. The engineer will lead performance strategy, design and execute complex test scenarios, analyze system behavior across layers, and influence architectural decisions to deliver high-performing, scalable solutions across hybrid cloud environments.

MUST HAVE:

• Perforemance Engineering experience (resolving issues after test)
• LoadRunner ENTERPRISE
• JMeter
• AWS experience (including Lambda, ECS)
• Backgroun in Java, * Lead end-to-end performance engineering activities-from requirement gathering and workload modeling to analysis, diagnosis, and optimization.
• Define performance KPIs, SLAs, SLOs, and non-functional requirements for enterprise applications.
• Mentor engineers, provide technical leadership, and drive performance engineering best practices across teams.

Cloud-Native Performance Engineering

• Design and execute performance tests for cloud-hosted, microservices-based, and containerized applications (AWS/Azure/Google Cloud Platform).
• Evaluate autoscaling policies, load-balancer behavior, API gateway performance, and cloud-native distributed workloads.
• Analyze cloud cost-performance tradeoffs and optimize system efficiency across compute, storage, and network layers.
• Use cloud-native observability platforms (CloudWatch, Azure Monitor, Google Cloud Platform Operations Suite) to collect and interpret performance telemetry.

AI-Driven Performance Optimization

• Apply AI/ML-based techniques to detect anomalies, predict bottlenecks, and identify performance degradation patterns.
• Use AI-enabled analytics tools to accelerate root-cause analysis and capacity forecasting.
• Integrate intelligent insights into dashboards, thresholding models, and performance reports.

Performance Testing & Automation

• Develop performance scripts using LoadRunner, JMeter, Gatling, Locust, K6, or equivalent frameworks.
• Automate performance pipelines via CI/CD tools (Jenkins, GitHub Actions, Azure DevOps).
• Build reusable, modular workloads to reflect real-world traffic patterns, data variations, and tenant behavior.

Performance Monitoring & Observability

• Utilize APM tools (Dynatrace, AppDynamics, New Relic, Datadog, Grafana) to monitor system behavior in real time.
• Define observability standards including telemetry instrumentation, distributed tracing, and structured logging.
• Develop dashboards that correlate performance metrics, traces, logs, and infrastructure KPIs.

Advanced Analysis & System Optimization

• Perform deep-dive performance diagnostics across application, database, messaging queues, caching layers, and integrations.
• Recommend tuning strategies for JVM/.NET, APIs, microservices, databases (SQL/NoSQL), and cloud resources.
• Evaluate resilience and scalability through chaos testing, failover tests, and peak readiness assessments.

Collaboration, Governance & SDLC Integration

• Partner with engineering, architecture, DevOps, SRE, QA, and product teams to ensure performance is built into design, not tested at the end.
• Drive non-functional requirements early in the SDLC via architecture reviews and design assessments.
• Maintain detailed performance documentation including baselines, reports, RCA documents, tuning recommendations, and capacity models.

Additional Responsibilities

• Identify, document, and track performance defects through JIRA.
• Stay current with evolving performance tools, cloud technologies, and AI/ML trends.
• Maintain regular and punctual attendance and perform related duties as assigned.

Supervisory Responsibilities

This is an individual contributor role with leadership responsibilities across technical domains and cross-functional collaboration, but no direct reports.

• Expert-level understanding of performance engineering principles, distributed systems, concurrency models, and cloud-native architectures.
• Strong analytical and diagnostic skills with the ability to interpret complex performance data.
• Proven ability to influence architecture and design decisions based on performance insights.
• Experience with AI/ML-based observability, anomaly detection, or predictive analytics is highly desirable.
• Knowledge of mortgage industry workflows, document automation, and rules engines is a plus., * Bachelor's degree in Computer Science, Engineering, or related field.
• 10-18 years of progressive experience in performance engineering and large-scale system optimization.
• Hands-on expertise with performance testing tools (LoadRunner, JMeter, Gatling, Locust, K6, etc.).
• Strong experience with cloud environments (AWS, Azure, or Google Cloud Platform).
• Solid understanding of databases (MongoDB, PostgreSQL, SQL Server, or NoSQL stores) from a performance standpoint.
• Experience with APM/observability platforms and log analysis tools.
• Experience integrating performance testing into CI/CD ecosystems.