Senior Software Engineering Manager - Pyspark, SQL & AI

JP Morgan Chase; Jersey City (NJ); On-site; USD 180,000 - 230,000; Full time

Job summary

JP Morgan Chase is seeking a Senior Software Engineering Manager in Corporate Technology to lead multiple engineering teams delivering data engineering and analytics capabilities for finance stakeholders. You will set technical direction, define delivery practices, and partner with business leaders to align roadmaps with outcomes.

You will also drive responsible AI-assisted engineering, ensure data sensitivity controls, and foster an inclusive culture focused on ownership and continuous

Qualifications

• Proven experience leading 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 BI tools.
• Ability to translate business outcomes into engineering roadmaps and delivery plans.
• Strong understanding of software fundamentals including code quality, testing, and production support.
• Effective stakeholder management across technology and business partners.
• Experience leading multi-team adoption of enterprise-authorized AI-assisted development and governance.

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 BI 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 and reducing incidents
• 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
• Set and scales operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across teams
• Apply SDLC tooling and AI-assisted development to drive efficiency and capacity unlock initiatives across teams

Skills

People leadership

Data engineering concepts

PySpark production

BI solutions

Roadmaps

Stakeholder management

AI-assisted development

Security and data sensitivity

Job description

Build what powers better decisions at scale. In this role, you’ll lead multiple engineering teams delivering modern data engineering and business intelligence capabilities that enable critical finance outcomes. You’ll create an environment where engineers do their best work—shipping reliably, raising quality, and learning continuously. You’ll also help scale responsible, enterprise-authorized AI-assisted engineering practices to unlock productivity while protecting security and data sensitivity. If you enjoy combining people leadership with technical depth and measurable impact, this is your opportunity.

Job Summary:

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 clear technical direction, operating practices, and delivery expectations across teams, balancing speed with quality and operational excellence. You will partner closely with business and technology leaders to prioritize outcomes and align roadmaps. You will build an inclusive culture focused on ownership, continuous improvement, and responsible use of enterprise-authorized AI capabilities.

Job 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
• 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

Required Qualifications, Capabilities, and Skills

• 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
• Strong 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 across technology and business partners
• 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 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