Manager of Software Engineering - Data Analytics

JPMorgan Chase & Co.•Jersey City, NJ

6d•$152,000 - $215,000

About The Position

This role is a challenge with big impact, but you were made for this. Bring your Engineering skills and lead and manage multiple technical teams and move financial technologies forward. As a Manager of Software Engineering at JPMorgan Chase within the Corporate - Data and Analytics team, where you will lead multiple teams and manage day-to-day implementation activities by identifying and escalating issues and ensuring your team’s work adheres to compliance standards, business requirements, and tactical best practices.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience. In addition, demonstrated coaching and mentoring experience
Experience leading technology projects
Experience managing technologists
Proficient in automation and continuous delivery methods
Proficient in all aspects of the Software Development Life Cycle
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
In-depth knowledge of the financial services industry and their IT systems
Practical cloud native experience
Experience working at code level
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 working with Java Springboot, Python, PiSpark, Mongodb, Oracle, Unix
Experience working with AI – generative or agentic and using them in design and coding.

Responsibilities

Provides guidance to immediate team of software engineers on daily tasks and activities
Sets the overall guidance and expectations for team output, practices, and collaboration
Anticipates dependencies with other teams to deliver products and applications in line with business requirements
Manages stakeholder relationships and the team’s work in accordance with compliance standards, service level agreements, and business requirements
Creates a culture of opportunity, inclusion, and respect for team members and prioritizes diverse representation
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.