Lead Software Engineer – Trading Tech + AI & Data Pipelines

Fairygodboss

Chicago (IL)

On-site

USD 140,000 - 210,000

Full time

14 days+

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Job summary

JPMorganChase in Chicago seeks a Lead Software Engineer to drive the design, development, and delivery of scalable market-leading technology products. You will lead agile teams, implement data pipelines and analytics, and champion AI-assisted engineering to improve code quality and delivery speed.

The role emphasizes secure production code, modern web stacks, and large-scale data processing across trading tech platforms. This position offers growth in a dynamic financial technology environment.

Qualifications

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.

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.