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Lead Software Engineer (Quant Developer)
lead · Technology / Software Development
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. Join a team where your contributions directly influence how the firm understands and acts on market data at scale. You'll work alongside talented engineers and quantitative professionals who are passionate about building cutting-edge solutions in a collaborative, inclusive environment where growth and innovation are encouraged.
As a Lead Software Engineer (Quant Developer) at JPMorganChase within the Commercial & Investment Bank's Electronic Trading Data Analytics team, you will be an integral part of an agile, global team building and enhancing trusted, market-leading technology products in a secure, stable, and scalable way. You will work on greenfield projects that analyze large volumes of data generated by the firm's trading engines, playing a pivotal role in shaping the next generation of analytics applications within the Equities division. As a core technical contributor, you will drive critical technology solutions across multiple technical areas in support of the firm's business objectives.
Job Responsibilities
- Design and execute creative software solutions, including architecture, development, and technical troubleshooting, thinking beyond conventional approaches to solve complex problems
- Develop secure, high-quality production code while reviewing and debugging code written by others to maintain engineering excellence
- Build and maintain core systems and frameworks using KDB+/Q and Python, ensuring reliability and performance at scale
- Serve as a subject matter expert in one or more areas of focus, providing technical guidance across the broader engineering community
- Actively champion firmwide frameworks, tools, and Software Development Life Cycle practices as an advocate within the engineering community
- Mentor junior team members, fostering a culture of learning, growth, and technical excellence
- Partner with the Product team to translate new business requirements into scalable, well-designed technical solutions
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
- Contribute to a team culture grounded in diversity, inclusion, opportunity, and mutual respect
Required Qualifications, Capabilities, and Skills
- Formal training or certification on software engineering concepts and advanced applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Advanced proficiency in KDB+/Q and Python for building and maintaining production-grade systems
- Proficiency across all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies, including CI/CD, application resiliency, and security practices
- Degree in Computer Science, Computer Engineering, Mathematics, or a related technical field
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices
Preferred Qualifications, Capabilities, and Skills
- Familiarity with cloud infrastructure (AWS) and containerization technologies
- Experience with Python data and scientific computing packages such as pandas, polars, NumPy, SciPy, or PyTorch
- Experience working with tick data and real-time data feeds in a trading or financial markets context
- Familiarity with workflow orchestration tools such as Airflow or pykx for building data pipelines
- Experience with C++ for building low-latency components
- Familiarity with FIX protocol, order types, and equities market microstructure