When you mentor and advise multiple technical teams and move financial technologies forward, it’s a big challenge with big impact. You were made for this.
As a Senior Manager of Software Engineering at JPMorganChase within Finance Technology (RAPIDS), you serve in a leadership role by providing technical coaching and advisory for multiple technical teams, as well as anticipate the needs and potential dependencies of other functions within the firm. As an expert in your field, your insights influence budget and technical considerations to advance operational efficiencies and functionalities.
Job responsibilities
- Provide overall direction, oversight, and coaching for a team of entry-level to mid-level software engineers that work on basic to moderately complex tasks
- Accountable for decisions that influence teams’ resources, budget, tactical operations, and the execution and implementation of processes and procedures
- 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.
- Ensures successful collaboration across teams and stakeholders
- Identifies and mitigates issues to execute a book of work while escalating issues as necessary
- Provides input to leadership regarding budget, approach, and technical considerations to improve operational efficiencies and functionality for the team
- Lead the end-to-end design and development of software solutions, from requirements to production deployment and support
- Serve as a technical owner for key components/services, driving architecture, code quality, performance, resilience, and security
- Build and maintain backend services using Java/Python (including Spring) and modern API patterns
- Deliver full stack capabilities, including front-end development using React, and integration with backend APIs
- Design and implement data pipelines and processing solutions leveraging Databricks (Spark-based patterns) and cloud data services
- Build cloud-native solutions on AWS, leveraging services such as EMR, Glue, ECS/EKS, RDS, Lambda, and Step Functions
- Design, integrate, and operationalize AI/ML and LLM-powered features (e.g., retrieval workflows, copilots/assistants, automation) with appropriate engineering rigor (testing, monitoring, governance alignment)
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience . In addition, 2 + years of experience leading technologists to manage and solve complex technical items within your domain of expertise
- Experience leading teams of technologists
- Ability to guide and coach teams on approach to achieve goals aligned against a set of strategic initiatives
- 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.
- Experience with hiring, developing, and recognizing talent
- Strong hands-on coding experience with Java and/or Python, including Spring for backend service development
- Full Stack development experience, including React and modern web development practices
- Practical cloud-native engineering experience, including deployment and operations on AWS
- Experience in AWS (EMR, GLUE, ECS/EKS, RDS, Lambda and Step Functions)
- Experience with data engineering/ETL and distributed processing; Databricks experience (e.g., Spark, jobs/workflows, Delta patterns)
- In-depth knowledge of the services industry and their IT systems
Preferred qualifications, capabilities, and skills