Senior Software Engineering Manager - Pyspark, SQL & AI

JPMorgan Chase; Jersey City (NJ); On-site; USD 180,000 - 240,000; Full time

Job summary

JPMorgan Chase in Jersey City is seeking a Senior Software Engineering Manager to lead multiple engineering teams delivering data engineering and business intelligence capabilities that support finance stakeholders.

You will set technical direction, promote ownership, and scale enterprise AI-assisted engineering while maintaining security and data sensitivity, partnering with business and technology leaders to prioritize outcomes.

Qualifications

• Demonstrated people leadership experience managing multiple engineering teams and leaders.
• Hands-on data engineering concepts, batch processing, and data pipeline design.
• Hands-on PySpark-based solutions in production environments.
• Experience delivering BI solutions using business intelligence tools.
• Experience delivering data and analytics 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 metrics for engineering throughput, quality, and reliability.
• 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
• 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

Skills

People leadership

Data engineering

PySpark

BI delivery

Finance domain

Reusable data products

Large-scale modernization

Engineering metrics

Distributed teams

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
• 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

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