Sr Lead Software Engineer – Data Engineering, Python/C++/KDB/AI
Headquartered in New York City, JPMorgan Chase is the largest bank in the United States.
You'll work with Python, KDB/C++, and AI technologies to design and optimize real-time data processing pipelines for JPMorgan's Electronic Trading Technology team within Commercial & Investment Bank. You'll lead technical initiatives across global analytics teams, building scalable solutions for mission-critical trading and research systems while mentoring engineers and driving SDLC improvements. This is a leadership role managing distributed teams in a fast-paced financial environment.
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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.