M
Not Specified Permanent

New York City, New York · USA job

General Ledger Technology - Executive Director

Morgan Stanley

New York City, New York

Job description

Overview

In this role you lead the global GLRD Technology function within Finance Technology, shaping strategy and delivering modern, data-driven platforms. You partner with Finance leadership to transform reference data, workflows, and AI-enabled processes across the firm. You'll build and mentor a high-performing, global engineering team, driving scalability, data quality, and operational control. This is a chance to impact how Morgan Stanley manages books, records, and financial data at scale.

Compensation / Benefits
  • attractive and comprehensive employee benefits and perks
  • ample opportunity to move about the business
  • supportive and empowering culture
Responsibilities
  • Manage the GLRD Technology team on a day-to-day basis
  • Define and execute the global GLRD technology strategy
  • Collaborate with Finance Reference Data leadership on priorities and transformation
  • Lead modernization of GLRD systems through automation and AI
  • Build and develop a high-performing global engineering team
  • Oversee architecture, design, development, testing, and deployment of GLRD platforms
  • Ensure system scalability, data quality, resiliency, and control
  • Promote best practices in workflow automation, DevOps, and data engineering
Key requirements
  • Finance domain expertise and experience in Finance Technology
  • Led teams of 15+ technologists in financial services or complex enterprises
  • Hands-on engineering leadership and problem-solving
  • Strong data engineering and data modelling experience
  • Excellent communicator with broad stakeholder influence
  • Strong listening, influencing, and relationship-building abilities
  • Highly organized, flexible, and able to operate in a fast-paced environment
  • Proven people manager with experience growing and motivating teams
  • Ability to manage large numbers of consumers with evolving requirements
  • Excellent communication
  • Strong listening and relationship-building
  • Adaptability and flexibility
  • Data engineering
  • Data modelling
  • Workflow automation

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