Java Performance Engineer

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Focuses on optimizing the performance, scalability, and efficiency of Spring Boot applications running across private cloud and AWS environments. Unlike manual testers or generic QA roles, this engineer operates at the code, framework, and JVM layer identifying bottlenecks in application logic, concurrency models, thread pools, memory usage, and data access paths. The role combines software engineering depth with advanced performance analysis techniques, ensuring applications meet strict latency, throughput, and reliability goals under real-world load.

Ideal candidates have extensive experience profiling high-throughput Java services and solving complex performance issues in Cloud environments.

• Perform deep JVM and application-level profiling using tools such as JFR, Your Kit, async-profiler, Perf, and flame graphs.
• Analyze and optimize Spring Boot internals including auto-configuration, dependency injection, async execution, serialization, and connection pool usage.
• Identify and resolve performance bottlenecks caused by inefficient code paths, excessive object allocation, and concurrency issues.
• Collaborate with developers to design scalable endpoints, improve API response times, reduce garbage creation, and improve overall memory efficiency.
• Debug and tune ORM frameworks (Hibernate/JPA) to eliminate N+1 queries, slow joins, unnecessary eager loading, or inefficient entity mappings.
• Create performance regression tests for critical services and integrate them into CI/CD pipelines.
• Provide guidance on coding standards, performance patterns, memory-safe programming, and best practices for low‑latency microservices.
• Mentor engineers on performance‑aware development and support teams during major performance incidents.

• Application & JVM: JVM internals, algorithms (G1, Z, Shenandoah), heap sizing strategies
• Spring Boot Performance:
  Data access optimization (JPA, JDBC, R2
  
  DBC), HTTP client tuning, connection pooling, serialization/deserialization
• Data Layer Performance: SQL tuning, indexing strategies, N+1 detection, cache design & validation (Redis, Hazelcast, Caffeine)
• Microservices & Cloud: PCF, Kubernetes, or ECS deployment performance; comparing performance differences between private cloud and AWS
• Performance Testing & Tooling:
  Load/stress testing (k6, Gatling, JMeter), profiling tools (JMC, JFR, async-profiler, Your Kit), benchmarking frameworks (JMH)