About the role
from listingLead data engineering teams building scalable analytics platforms, using responsible AI to accelerate delivery.
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