Senior Manager of Software Engineering -Front office trading

JPMorgan Chase & Co.•Jersey City, NJ

3d•$171,000 - $260,000

About The Position

Lead a high-impact engineering team in JPMorgan Chase’s TCIO Trade Execution Technology. Partner with senior stakeholders to raise engineering standards and automate recurring issues—driving platform stability and business impact across fixed income and derivatives. As a Manager of Software Engineering at JPMorgan Chase in the Corporate Technology - Treasury & Chief Investment Office Technology team, you play a pivotal leadership role by providing advanced technology solutions both individually and as a technical coach and mentor to a team of talented and driven software engineers. Leveraging your expertise, you oversee essential technology initiatives across various technical domains within the Trade Execution technology team, supporting the business goals of the Treasury & Chief Investment Office (TCIO).

Requirements

Formal training or certification on software engineering concepts and 5 years of applied experience
Hands-on practical experience delivering system design, application development, testing, and operational stability
Advanced in one or more programming language(s)- Java or Python
Hands-on experience with AWS cloud services (e.g., EC2, S3, Lambda, RDS, etc.).
Experience designing, developing, and deploying scalable applications in cloud environments.
Familiarity with microservices architecture and RESTful API development is needed .
Experience with containerization and orchestration tools (e.g., Docker, Kubernetes).
Experience with modern monitoring and logging tools (e.g., DataDog, Dynatrace, Splunk).
Experience with software development best practices, including code reviews, testing, and CI/CD pipelines.
Experience leading multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including defining governance/ways of working (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and control expectations; ability to coach managers/leads and influence leaders on safe scaling patterns.

Nice To Haves

Experience in architecting large-scale, distributed systems.
Ability to drive technical strategy and influence engineering best practices across teams.
Strong stakeholder management and the ability to align technical solutions with business goals.
Prior experience supporting trade execution platforms and familiarity with investment banking products - fixed income and derivatives

Responsibilities

Provides guidance to the immediate team of software engineers on daily tasks and activities to execute creative software solutions, including design, development, and technical troubleshooting, with the ability to think beyond routine or conventional approaches.
Develops secure high-quality production code and sets the overall guidance and expectations for team output, practices, and collaboration.
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.
Anticipates dependencies with other teams and leads evaluation sessions with external vendors and internal teams to drive outcome-oriented designs and ensure applicability within existing systems and information architecture.
Manages stakeholder relationships and the team’s work in accordance with compliance standards, service level agreements, and business requirements.
Creates a culture of diversity, opportunity, inclusion, and respect for the team member's.
Sets and scales operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams to improve delivery speed, quality, and operational outcomes; establishes measurable expectations (e.g., throughput, defect reduction, reliability) and ensures consistent validation, security, resiliency, and reuse of proven patterns.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive efficiency and support capacity unlock initiatives across teams, prioritizing reuse of existing firm technology assets.