Senior Software Engineering Manager - Pyspark, SQL & AI

JPMorganChase•Jersey City, NJ

About The Position

As a Senior Software Engineering Manager in the Corporate Technology, Asset & Wealth Management Finance team, you will lead multiple engineering teams delivering data engineering and business intelligence solutions that support finance stakeholders. You will set technical direction, operating practices, and delivery expectations across teams, balancing speed with quality and operational excellence. You will partner with business and technology leaders to prioritize outcomes and align roadmaps, and foster an inclusive culture focused on ownership, continuous improvement, and responsible use of enterprise-authorized AI capabilities. The role includes scaling AI-assisted engineering practices to improve productivity while protecting security and sensitive data.

Requirements

Demonstrated people leadership experience managing multiple engineering teams and leaders.
Hands-on knowledge of data engineering concepts, including batch processing patterns and data pipeline design.
Hands-on experience building or supporting PySpark-based solutions in production environments.
Experience delivering business intelligence solutions using business intelligence tools.
Ability to translate business outcomes into actionable engineering roadmaps and delivery plans.
Strong understanding of software engineering fundamentals, including code quality, testing, and production support.
Effective stakeholder management skills, including influencing technology and business partners.
Experience leading multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including defining governance and ways of working, human-in-the-loop validation, and quality gates; measuring outcomes; and ensuring secure handling of sensitive inputs and outputs.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency and security implications, and control expectations.
Ability to coach managers and leads and influence leaders on safe scaling patterns.

Nice To Haves

Experience delivering data and analytics solutions in a finance-aligned domain.
Experience building reusable data products and enabling self-service analytics at scale.
Experience leading large-scale modernization initiatives across teams.
Familiarity with establishing metrics for engineering throughput, quality, and reliability across organizations.
Experience partnering with geographically distributed teams and stakeholders.

Responsibilities

Lead multiple teams delivering data engineering platforms and analytics solutions aligned to finance priorities.
Define and execute the data engineering roadmap, including scalable pipeline design and modernization.
Architect and guide PySpark-based processing patterns to improve performance, reliability, and maintainability.
Deliver business intelligence capabilities by partnering with stakeholders to define requirements and drive adoption.
Establish engineering standards for code quality, testing, documentation, and production readiness.
Drive operational excellence by monitoring service health, reducing incidents, and improving recoverability.
Develop talent through coaching, feedback, hiring, and succession planning across teams.
Partner with risk, security, and governance stakeholders to ensure appropriate handling of sensitive data.
Scale operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across teams, improving delivery speed, quality, and operational outcomes.
Establish measurable expectations such as throughput, defect reduction, and reliability, and ensure consistent validation, security, resiliency, and reuse of proven patterns.
Use software development lifecycle toolchain capabilities, including enterprise-authorized AI-assisted development and automation, to drive efficiency and support capacity-unlock initiatives while prioritizing reuse of existing firm technology assets.