Java Lead Software Engineer, Asset Wealth Management

JPMorgan Chase

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Quick summary

Work type: On-site
Location: Columbus, OH
Employment: Full-time
Posted: 116 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $166k

$123k $220k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $133,668–$198,125.

Based on 240 similar postings.

Employer

About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1226 open roles on FindRole.

Listed pay typically runs $156,750–$215,000 across 5 roles with salary data.

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At a glance

TL;DR · Java Lead Software Engineer, Asset Wealth Management

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As a Java Lead Software Engineer - Asset Wealth Management, you will join a hands-on engineering team within Morgan Money to build next-generation liquidity trading capabilities for corporate clients. You will partner with investment and trading stakeholders to design, build, and operate low-latency, secure-by-design backend services. Your daily responsibilities include owning end-to-end feature delivery, driving architectural decisions for scalability, and establishing disciplined engineering practices like automated testing and continuous integration. You will utilize Java, Spring frameworks, Kafka, and Python while implementing enterprise-authorized AI-assisted engineering practices for code review and test acceleration. The role focuses on solving complex technical problems in liquidity trading by improving observability, performance engineering, and resiliency across distributed systems to ensure high-performing, reliable outcomes for the Asset & Wealth Management business area.

Skills

Java Spring Kafka Python Distributed Systems Contract Testing Observability Metrics Tracing Logging Event-driven Design Object-Oriented Design AI-assisted Engineering Secure Coding Performance Engineering

What you'll do

Translate business objectives from investment and trading teams into durable technical outcomes.
Design, build, and operate low-latency, scalable, and secure backend services using Java.
Own end-to-end feature delivery from initial design through production release and operational readiness.
Drive architecture decisions to improve performance, reliability, and long-term maintainability of services.
Establish engineering practices including automated testing, code reviews, and continuous integration/delivery.
Improve observability and incident readiness through logging, metrics, tracing, and production diagnostics.
Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and speed.
Apply SDLC toolchain automation to improve engineering productivity and delivery value.

What we're looking for

Formal training or certification on software engineering concepts and 5+ years of applied experience.
Hands-on experience delivering production-grade backend services using Java with strong object-oriented design and debugging skills.
Experience building scalable distributed systems with a focus on low-latency performance, resiliency patterns, and failure-mode thinking.
Proficiency with modern Java frameworks like Spring and dependency injection patterns to build maintainable services.
Demonstrated experience implementing strong testing practices including unit, integration, and contract testing with automated quality gates.
Experience with messaging and streaming technologies like Kafka and event-driven design patterns.
Strong understanding of secure coding practices and the ability to design and implement controls that reduce operational and technology risk.
Demonstrated experience leading the use of approved AI-assisted software development tools and coaching engineers on safe, compliant adoption.
Experience building and operating services on cloud platforms (preferred).
Proficiency in performance engineering including profiling, latency analysis, and concurrency tuning (preferred).
Experience with observability platforms and practices such as metrics, tracing, and alerting (preferred).
Familiarity with liquidity, trading, or capital markets concepts (preferred).
Proficiency in Python for automation, tooling, or data analysis (preferred).

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