We have an exciting and rewarding opportunity for you to take your engineering career to the next level.

As a Senior Lead Software Engineer - Performance Engineer at JPMorgan Chase within the Commercial & Investment Bank, you serve as a seasoned member of an agile team to ensure trusted market-leading technology products operate at optimal performance, scalability, and reliability. You are responsible for carrying out critical performance engineering solutions across multiple technical areas within various business functions in support of the firm's business objectives.

Job responsibilities

Executes performance testing, analysis, and engineering solutions with the ability to think beyond routine or conventional approaches to identify and resolve performance bottlenecks
Designs, develops, and maintains performance test scripts and frameworks to validate system scalability, reliability, and responsiveness under varying loads
Produces architecture and design artifacts for performance optimization, ensuring that performance constraints are met throughout the software development lifecycle
Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets to drive continuous improvement of application and system performance
Identifies hidden performance issues proactively and patterns in data, using these insights to drive improvements to system architecture and coding practices
Collaborates with software engineering, infrastructure, and business teams to define performance requirements and ensure alignment with business goals
Contributes to engineering communities of practice and events that explore new and emerging performance engineering tools and methodologies
Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.

Required qualifications, capabilities, and skills

Formal training or certification on performance engineering concepts and 3+ years applied experience in performance testing and engineering
Proficient in scripting and programming languages such as Java, Python, or similar, with strong analytical and troubleshooting skills
Hands-on experience with performance testing tools such as JMeter, LoadRunner, or Gatling
Experience with monitoring and profiling tools (e.g., AppDynamics, Dynatrace, Grafana, or similar) for root cause analysis and performance tuning
In-depth knowledge of system architecture, application development, and operational stability
Experience with cloud infrastructure such as AWS or Kubernetes, and understanding of how cloud environments impact application performance
Strong understanding of databases (e.g., PostgreSQL, Oracle, or similar) and experience in analyzing and optimizing database performance
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations experience coaching senior engineers/leads on compliant usage patterns and controls.

Preferred qualifications, capabilities, and skills

Familiarity with modern front-end technologies and their impact on end-to-end system performance
Exposure to CI/CD pipelines and integrating performance testing into automated workflows
Experience with containerization and microservices architectures
Strong interest in AI/ML-driven performance analytics
Knowledge of caching strategies and distributed systems