> Markdown version of [/jobs/ext/3930970-manager-of-software-engineering-data-analytics](https://www.wearedevelopers.com/jobs/ext/3930970-manager-of-software-engineering-data-analytics). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Manager of Software Engineering - Data Analytics - **Company:** JPMorgan Chase & Co. - **Location:** Jersey City, NJ, United States - **Experience:** Expert - **Salary:** $156,750.0 - $215,000.0 - **Contract:** Permanent contract - **Skills:** Agile Methodology, Artificial Intelligence, Unix, Continuous Delivery, Continuous Integration, Python (Programming Language), MongoDB, Oracle (Applications), Systems Development Life Cycle, Software Engineering, Toolchain, Spring-boot, Pyspark, Information Technology, Data Analytics - **Published:** October 26, 2026 - **Apply:** https://www.techcareers.com/job.asp?id=3468501036&tx=ZT212TYI&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role * 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. Preferred qualifications, capabilities, and skills * Experience working with Java Springboot, Python, PySpark, MongoDB, Oracle, Unix * Experience working with AI - generative or agentic and using them in design and coding. ## Description 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., * 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.