Sr Manager of Software Engineering - Python & Databricks

This is your chance to change the path of your career and guide multiple teams to success at one of the world's leading financial institutions. As a Senior Manager of Software Engineering at JPMorgan Chase within the Corporate Technology, you will serve in a leadership role by providing technical coaching and advisory for multiple agile technical teams. You will anticipate the needs and potential dependencies of other functions within the firm. As an expert in your field, your insights will influence budget and technical designs to advance operational efficiencies and functionalities.

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 Leads team adoption of enterprise-authorized AI-assisted engineering practices and SDLC/TLM automation to improve delivery speed, quality, and operational outcomes, while setting expectations for human validation, secure handling of inputs/outputs, and consistent use of reusable patterns across teams. 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 and support capacity unlock initiatives. 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 Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and applied experience. Proven end-to-end delivery of data products using Python and/or Spark and Databricks. Experience building data-intensive software application Experience managing technologists Experience leading responsible adoption of enterprise-authorized AI-assisted development and delivery tools across engineering teams, including defining ways of working (review/validation expectations), measuring outcomes, and ensuring secure handling of data. Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and governance expectations; ability to coach engineers on compliant and effective usage. 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