At a Glance
- Tasks: Lead innovative software projects in a fast-paced financial environment using Python, C++, and AI.
- Company: Join JPMorgan Chase's dynamic global analytics team in Electronic Trading Technology.
- Benefits: Competitive salary, diverse work culture, and opportunities for professional growth.
- Other info: Mentorship opportunities and a focus on diversity and inclusion.
- Why this job: Make a real impact on trading technology while collaborating with global teams.
- Qualifications: 5+ years in software engineering with expertise in Python, KDB, or C++.
The predicted salary is between 80000 - 100000 £ per year.
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.
Sr Lead Software Engineer – Data Engineering, Python/C++/KDB/AI in London employer: JPMorganChase
At JPMorgan Chase, we pride ourselves on being an exceptional employer, offering a vibrant work culture that fosters innovation and collaboration within our global analytics team. As a Senior Lead Software Engineer, you will benefit from extensive opportunities for professional growth, mentorship, and the chance to work with cutting-edge AI technologies in a fast-paced financial environment. Our commitment to diversity and inclusion ensures that every voice is heard, making it a rewarding place to advance your career while contributing to impactful projects.
StudySmarter Expert Advice🤫
We think this is how you could land Sr Lead Software Engineer – Data Engineering, Python/C++/KDB/AI in London
✨Network Like a Pro
Get out there and connect with folks in the industry! Attend meetups, webinars, or even just grab a coffee with someone who works at JPMorgan Chase. Building relationships can open doors that a CV just can't.
✨Show Off Your Skills
When you get the chance to chat with recruiters or during interviews, don’t hold back! Share specific examples of how you've used Python, KDB, or AI in your projects. We want to see your passion and expertise shine through!
✨Tailor Your Approach
Before any interview, do your homework! Understand the latest trends in data engineering and trading tech. This way, you can speak their language and show that you're not just another candidate, but the right fit for their team.
✨Apply Through Our Website
Don’t forget to apply directly through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our awesome team at JPMorgan Chase.
We think you need these skills to ace Sr Lead Software Engineer – Data Engineering, Python/C++/KDB/AI in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the role. Highlight your experience with Python, KDB, and C++, and don’t forget to mention any AI projects you've worked on. We want to see how your skills align with what we’re looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to tell us why you’re passionate about data engineering and how your background makes you a perfect fit for our team. Keep it engaging and relevant to the job description.
Showcase Your Projects:If you’ve worked on any relevant projects, make sure to include them in your application. Whether it's a personal project or something from your previous job, we love seeing practical examples of your work and how you’ve leveraged AI in your solutions.
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to keep track of your application status. Plus, we love seeing candidates who take that extra step!
How to prepare for a job interview at JPMorganChase
✨Know Your Tech Inside Out
Make sure you brush up on your Python, KDB, and C++ skills. Be ready to discuss specific projects where you've used these technologies, especially in real-time data processing or analytics. They’ll want to see how you’ve applied your knowledge in a fast-paced environment.
✨Showcase Your AI Experience
Since leveraging AI is crucial for this role, prepare examples of how you've integrated AI into your previous work. Whether it's automating processes or enhancing data workflows, be specific about the impact your contributions had on the project.
✨Demonstrate Leadership Skills
As a Lead Software Engineer, you'll need to guide teams. Think of instances where you've mentored others or led technical initiatives. Highlight your ability to manage projects and drive continuous improvement in SDLC and coding standards.
✨Prepare for Collaboration Questions
Expect questions about working with global teams and onboarding new datasets. Be ready to discuss how you’ve collaborated with research and trading teams, and how you ensure consistency and efficiency in your work across different locations.
