
Lead Software Engineer - Markets, Java, AI, Spring Boot
JPMC · Chicago, IL, United States
Strong hiring record · median 28 days open
Hiring signal
Actively hiring
Still listed on the employer's own board · publishes a salary range · How we score
All reasons
In its favour
- Still listed on the employer's own board
- Publishes a salary range
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorganChase within the Commercial & Investment Bank - Trading Technology team, 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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Designs, develops, and troubleshoots creative software solutions, thinking beyond conventional approaches to solve complex technical problems
- Leads the adoption and integration of cutting-edge technologies including Java, Spring, Springboot, React, and Kafka
- Develops secure, high-quality production code, and reviews and debugs code written by others
- Defines and drives the technology roadmap, focusing on next-generation data, analytics, and automation platforms
- Champions the use of AI, machine learning, and advanced analytics to deliver real-time trade processing applications
- Identifies opportunities to eliminate or automate recurring issues to improve overall operational stability of software applications and systems
- Designs and implements data pipelines, analytics workflows, and reporting solutions using Python, Databricks, and Spark
- Develops and maintains web-based applications and dashboards using modern web stacks (React, Angular, etc.)
- Builds and optimizes data models, ETL/ELT processes, and integration frameworks for large-scale financial data
- Ensures data quality, security, lineage, and governance across all team platforms
- 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.
Required qualifications, capabilities, and skills
- Formal training or certification in software engineering concepts with 7+ years applied experience
- Hands-on practical experience in system design, application development, testing, and maintaining operational stability
- Hands-on expertise in Java, Spring, Springboot, Maven, Kafka, and modern web technologies such as React
- Strong knowledge of cloud platforms such as AWS, Lambda, ECS, EC2, and Terraform
- Proficiency in automation and continuous delivery, including CI/CD tools such as GitHub Actions and AWS CodePipeline
- Advanced proficiency in designing scalable, secure, and resilient systems for large-scale data environments
- Demonstrated ability to lead engineering teams, set technical direction, and deliver complex projects
- Hands on experience in leading and implementing standard software engineering practices, such as Agile development, code reviews, automated testing, CI/CD pipelines, and documentation
Preferred qualifications, capabilities, and skills
- Proven experience in financial markets, Futures & Options, and Trade Execution/Clearing
- Knowledge of AMPS (messaging platform), MQ, Cucumber Integration Tests, JUnit, Mockito, and Testcontainers
Skills
- Java
- Spring Boot
- Spring
- React
- Kafka
- Machine Learning
- Python
- Databricks
- Spark
- Angular
- ETL
- ELT
- Maven
- AWS
- AWS Lambda
- ECS
- EC2
- Terraform
- GitHub Actions
- AWS CodePipeline
- Cucumber
- JUnit
- Mockito
- Testcontainers
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