Manager of Software Engineering - Spark, AWS, Databricks, Java

Job description

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 Manager of Software Engineering at JPMorganChase within the Enterprise Technology, you 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.

Job 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
• 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 5+ years applied experience.

In addition, demonstrated coaching and mentoring experience Experience leading technology projects with Big Data & Event Driven Architecture.

Experience & hands on with technology

Spark, Databricks, AWS, Java 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 Preferred qualifications, capabilities, and skills Exposure to any of decision engines like ODM, Drools