
Lead Software Engineer ARKET RISK - EMEA
at J.P. Morgan
Posted 17 hours ago
No clicks
- Compensation
- Not specified
- City
- London
- Country
- United Kingdom
Currency: Not specified
Hands-on Python engineer partnering with Market Risk stakeholders to rapidly design, prototype, and productionize VaR and Stress utilities and services. Lead ideation and problem framing, deliver MVPs that integrate with firm platforms (familiarity with Athena or SecDB a plus), and uphold engineering standards including CI/CD, observability, and runbooks. Use LLMs to accelerate prototyping, documentation, and operational support while streamlining manual steps through automation. Communicate effectively with quants, business, and technology teams and own delivery from discovery through deployment and support.
Location: LONDON, United Kingdom
We are seeking a dynamic, hands-on Python engineer to partner with Market Risk stakeholders and deliver practical solutions quickly. You will shape priorities, contribute ideas, and drive decisions while staying deeply involved in the code. Familiarity with Athena or SecDB is a strong plus. You think clearly and communicate exceptionally.
Role Description
Market Risk Technology supports JPMorgan’s Risk Managers in understanding firmwide exposures and running core risk processes. In this RAD role, you will help identify the right problems to solve, propose options, brainstorm with stakeholders, and turn MVPs into reliable production solutions that integrate with existing platforms. You will contribute to workflows related to VaR and Stress, and you will own delivery from discovery through deployment and support. This is a senior individual contributor role with meaningful influence.
Job Responsibilities
- Lead ideation and problem framing, present options and trade-offs, and help teams converge on the right solutions
- Stay hands-on in Python to design, implement, and productionize utilities and services that integrate with firmwide platforms, including Athena or SecDB
- Use LLMs effectively as an AI generalist to speed up prototyping, documentation, code assistance, and operational support
- Streamline manual steps and reduce cycle time through thoughtful automation and simplification
- Uphold engineering standards across design, coding, testing, CI/CD, observability, documentation, and runbooks
- Maintain operational stability with pragmatic monitoring, logging, and metrics
- Communicate clearly across business stakeholders, quant teams, and technology teams; present demos and drive alignment
- Foster a culture of collaboration, diversity, equity, inclusion, and respect; share best practices and improve team ways of working
- Continuously learn, improve, and help others do the same
Required Qualifications, Capabilities, and Skills
- Python required, with strong OO fundamentals and clean coding practices
- Strong knowledge of the software development life cycle and Agile delivery
- Experience building APIs and services and working with database querying languages
- Exceptional communication skills, with the ability to converse with senior business stakeholders, quants, and technology leaders
- Familiarity with Market Risk domain concepts, including VaR and Stress
- Ability to use LLMs effectively; AI generalist mindset focused on practical value
Preferred Qualifications, Capabilities, and Skills
- Familiarity with Athena or SecDB and related integration patterns
- Exposure to modern front-end technologies
- Banking and financial services domain exposure is a big plus
- Experience improving engineering practices through code reviews, standards, and mentoring
- Hands-on operational practices such as logging, metrics, tracing, and clear runbooks
What Success Looks Like
- You help shape the roadmap through clear thinking, practical ideas, and effective stakeholder dialogue
- You deliver MVPs in weeks that reduce manual effort and improve productivity, then mature them into stable services
- Your work improves reliability and supportability and is accompanied by clear documentation and runbooks
- You elevate collaboration and communication across business, quants, and technology while modeling an inclusive, learning-oriented culture





