Sr Lead Software Engineer – Data Engineering, Python/C++/KDB/AI

JPMorgan Chase & Co.; Greater London; On-site; GBP 110,000 - 180,000; Full time

Job summary

JPMorgan Chase & Co. in London is seeking a Lead Software Engineer for Electronic Trading Technology. You will design and deliver high-performance, scalable real-time data processing and analytics pipelines fed by AI-enhanced data engineering.

You will mentor engineers, drive SDLC improvements, and collaborate with global teams to onboard new datasets and deliver production-grade systems in a fast-paced financial environment.

Qualifications

• 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.
• Academic background in Computer Science, Computer Engineering, Mathematics, or a related technical field.

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.

Skills

Python

KDB

C++

AI Technologies

CI/CD

Leadership

Mentoring

Education

Bachelor's degree in Computer Science/Engineering

Tools

Kubernetes

Terraform

AWS

Job description

Join adynamic, global analytics teamwithin 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 inany of Python/KDB/C++, and who can leverage their knowledge ofAIto 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 initiativesacross 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 techniquesto enhance data engineering workflows, automate SDLC processes, and deliver advanced analytics capabilities for trading and research.
• Collaborate with research and trading teams worldwideto onboard new datasets efficiently and consistently, supporting global business needs.
• Build and support robust tools and frameworksfor 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/processesto meet the demands of a rapidly evolving financial landscape.
• Serve as a subject matter expertin Python, KDB/Q, data engineering, and AI, contributing to firmwide best practices and technical excellence.
• Champion diversity, inclusion, and collaborationwithin 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.