Sr. Lead Software Engineer - Electronic trading, Python

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Senior Lead Software Engineer at JPMorganChase within the Electronic Trading Technology, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

Job responsibilities 

• Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
• 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. 
• 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.
• Influences peers and project decision-makers to consider the use and application of leading-edge technologies
• Adds to the team culture of diversity, opportunity, inclusion, and respect

Required qualifications, capabilities, and skills 

• Formal training or certification on software engineering concepts and 5+ years applied experience
• 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. Demonstrated experience leading effective use of approved AI-assisted software development tools (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 engineers on safe, compliant adoption within delivery practices
• 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. 
• Experience in Computer Science, Computer Engineering, Mathematics, or a related technical field
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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.