Principal Performance Engineer

Candidates must be in St.Louis (Local resource) or should be 100% willing to relocate - will work 3 to 4 days onsite

Client has a performance engineering team that evaluates the Transaction system, however, there are gaps in experience and maturity of the team.

In this role, the lead will be responsible for identifying and resolving end-to-end performance bottlenecks across distributed systems, Spring Boot services, middleware components, and hybrid cloud environments (private cloud + AWS). This role goes far beyond traditional testing by deeply analyzing container orchestration, networking paths, and system interactions under load. This position maps full system workflows, sets realistic latency budgets, and ensures each component meets its SLOs.

Ideal candidates have extensive experience with high-scale, multi-region, and high-transaction platforms (e.g., financial systems, payment processing, or large enterprise SaaS) running in a Cloud environment.

• Define service-level objectives (SLOs), performance budgets, and latency/throughput targets across services.
• Architect and champion comprehensive distributed tracing strategies (Dynatrace, AWS X-Ray, etc.).
• Analyze application, platform, and cloud behavior using deep-dive techniques such as heap dumps, thread dumps, flame graphs, GC logs, network traces, and storage I/O profiling.
• Review service and system architectures for performance risks (e.g., synchronous hops, excessive dependencies, misconfigured connection pools, poor cache placement).
• Conduct and lead root-cause analysis for performance incidents in production and pre-production environments.
• Develop capacity models and performance baselines for services running across cloud environments.

• Application Layer:
  Spring Boot internals, JVM tuning, thread/heap management, concurrency debugging, GC optimization
• Container Runtime: PCF, Docker, container resource limits, CPU throttling, memory pressure
• Storage:
  Latency, IOPS constraints, distributed file system behavior
• Caching & Middleware:
  Redis, Hazelcast, NATS, Kafka, RabbitMQ configuration and throughput tuning
• Databases:
  Connection pool tuning, slow queries, indexing, replication lag
• Cloud Layer: AWS compute/storage/network performance, regional latency, cross-cloud traffic patterns