Performance Engineer

TEKsystems•Dallas, TX

8d•$65 - $75•Hybrid

About The Position

The Performance Engineer is an individual contributor on the Performance Engineering team, independently planning and delivering performance, scalability, resiliency, and capacity initiatives for a Java-based trading platform. The role focuses on latency, reliability, scalability, and resiliency across client, server, gateway, infrastructure, and cloud environments. The engineer builds workloads, tools, test frameworks, and automation; identifies code and system bottlenecks; and recommends evidence-based improvements. The work includes reading source code, diagnosing JVM and system behavior, and using production traffic patterns to inform workload design, capacity planning, risk analysis, and validation. The position is a contract role based in Dallas, Texas, with a hybrid workplace.

Requirements

Bachelor's degree in computer science or engineering.
At least 5 years of experience in performance, scalability, software engineering, or production performance analysis.
Strong experience with performance, scalability, failover, recovery, and resiliency testing.
At least 2 years of hands-on Java development experience.
Deep Core Java knowledge, including JVM internals, concurrency, multithreading, memory, garbage collection, and latency; ability to analyze source code and recommend code-level improvements.
Ability to read source code and diagnose bottlenecks using dumps, logs, profilers, metrics, and observability tools.
Experience building workload generators, utilities, mock services, or automation using Java, Scala, Python, shell, JMeter, Gatling, or similar tools.
Strong knowledge of Linux, databases, application servers, caching, connection pools, distributed systems, and networks.
Ability to create workloads, baselines, thresholds, capacity measures, acceptance criteria, SLOs, SLAs, and KPIs.
Ability to provide evidence-based code, configuration, capacity, reliability, and design recommendations.
Experience using Splunk, Grafana, or other monitoring tools.
Experience testing resiliency solutions by updating circuit breaker, shaper, load balancer, and failover configurations.
Experience troubleshooting and diagnosing JVM issues, including thread dumps, garbage collection, and memory management.
Understanding of performance best practices, key performance metrics, and statistics.
Ability to manage priorities and work with distributed teams.

Nice To Haves

Experience with real-time financial systems or trading.
Experience with Google Cloud, Kubernetes, or Docker.
Experience with AI-assisted analysis, LLM tools, or intelligent automation.
Experience using AI tools with Visual Studio or IntelliJ IDEA.
Experience with JVM tuning, heap dumps, memory leak analysis, JProfiler, YourKit, VisualVM, Flight Recorder, or Java Mission Control.
Experience with circuit breaker technologies such as Hystrix or Resilience4j, chaos engineering, and caching systems such as Redis, Hazelcast, or Ignite.
A Java Developer or Site Reliability Engineer background.

Responsibilities

Independently plan and deliver performance, scalability, resiliency, and capacity validation for assigned systems.
Learn system architecture, business flows, dependencies, workloads, and production behavior.
Define requirements, workloads, acceptance criteria, and reports with partner teams.
Build performance test scripts, workload generators, mock services, frameworks, and automation.
Run load, stress, endurance, scalability, failover, recovery, degradation, and resiliency tests.
Analyze source code, JVM behavior, system resources, databases, networks, logs, metrics, and end-to-end latency.
Recommend and validate code, configuration, infrastructure, database, capacity, design, and resiliency improvements, including circuit breakers, shapers, load balancers, and failover.
Use production behavior as the baseline for workload design, capacity planning, risk analysis, and validation.
Maintain dashboards, reports, diagnostic tools, runbooks, frameworks, and workload libraries.
Evaluate new testing, profiling, observability, and resiliency tools.
Use approved automation and AI tools to improve scripting, analysis, documentation, and diagnostics.
Communicate results, risks, recommendations, and open issues clearly.