Join a dynamic, global analytics team within JPMorgan Chase’s Commercial & Investment Bank, Electronic Trading Technology.
As a Lead Software Engineer at JPMorgan Chase within Commercial & Investment Bank, Electronic Trading Technology, you will play a pivotal role in designing and delivering high-performance, scalable solutions that power real-time trading and research in a fast-paced financial environment. We seek candidates with strong expertise in any of Python/KDB/C++, and who can leverage their knowledge of AI to drive innovation in data engineering, analytics, and automation.
Experience leveraging AI in development, analytics, or SDLC use cases is a critical enabler for this role.
Job Responsibilities
- Lead technical initiatives across global analytics teams, providing guidance and direction to engineers, contractors, and vendors in a high-velocity environment.
- Design, build, and optimize real-time data processing pipelines and applications ensuring reliability and performance for mission-critical financial systems.
- Leverage AI technologies and techniques to enhance data engineering workflows, automate SDLC processes, and deliver advanced analytics capabilities for trading and research.
- Collaborate with research and trading teams worldwide to onboard new datasets efficiently and consistently, supporting global business needs.
- Build and support robust tools and frameworks for quantitative research and production trading, including scalable APIs and analytics libraries.
- Mentor and develop team members, manage book of work, and drive continuous improvement in SDLC, testing, and coding standards across distributed teams.
- Influence product design, application functionality, and technical operations/processes to meet the demands of a rapidly evolving financial landscape.
- Serve as a subject matter expert in Python, KDB/Q, data engineering, and AI, contributing to firmwide best practices and technical excellence.
- Champion diversity, inclusion, and collaboration within large, global teams.
Required Qualifications, Capabilities, and Skills
- 5+ years of applied experience in software engineering, in large-scale, fast-paced financial environments.
- Hands-on experience delivering system design, application development, testing, and operational stability for analytics-driven teams.
- Strong expertise in any of Python/KDB/C++, for real-time data processing, application development, or data engineering.
- Working knowledge of AI technologies (machine learning, generative AI, etc.) to support data engineering, analytics, or SDLC automation.
- Proficiency in automation and continuous delivery methods; advanced understanding of agile methodologies (CI/CD, Application Resiliency, Security).
- Experience leading and mentoring teams in a global, collaborative environment.
- Ability to tackle complex design and functionality problems independently and drive solutions across distributed teams.
- Academic background in Computer Science, Computer Engineering, Mathematics, or a related technical field.
Preferred Qualifications, Capabilities, and Skills
- Experience with market data venue and vendor data platforms.
- AWS experience; practical cloud native/cloud experience is a plus.
- Experience with Terraform and Kubernetes for managing production environments in public cloud.
- Strong knowledge and experience in FIX, Market Data, Analytics, OMS, and equities trading in global markets are assets.
- Knowledge of machine learning, statistical techniques, and related libraries.
