Vice President - Lead Data Engineering & AI - Morgan Stanley

Vice President – Lead Data Engineering & AI

Profile Description

We’re seeking someone to join our FRPPE Tech team as Lead Data Engineering & AI in Finance Technology to lead the design, development, and implementation of enterprise-scale data warehouse, reporting, analytics, and AI-enabled data solutions, preferably on cloud platforms such as Snowflake.

This role requires a strong leader with a blend of deep data engineering expertise and applied AI / GenAI architecture experience to build intelligent, scalable solutions across finance data platforms. The ideal candidate will help shape the next generation of data products by enabling natural language interaction with enterprise data, retrieval-augmented generation (RAG), LLM orchestration, agent-based workflows, and evaluation frameworks for safe and effective AI adoption.

About Finance Technology

Finance Technology at Morgan Stanley delivers innovative solutions for regulatory and financial reporting, general ledger, P&L calculations, and analytics. The team leverages advanced data platforms, modern engineering practices, and is a pioneer in leveraging GenAI for finance productivity—building innovative solutions for automation, insight generation, and efficiency.

In the Technology division, we leverage innovation to build the connections and capabilities that power our Firm, enabling our clients and colleagues to redefine markets and shape the future of our communities.

This is a Lead Data Engineering & AI role within the job family responsible for developing and maintaining software solutions that support business needs, with an expanded mandate to drive AI-powered data access, automation, and decision-support capabilities.

Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and communities in more than 40 countries around the world.

What you’ll do in the role

Responsibilities

Lead the architecture, design, and implementation of enterprise-scale data platforms, including data warehousing, semantic modeling, reporting, analytics, and data distribution solutions.

Drive the adoption and integration of GenAI, LLMs, and modern AI/ML techniques for ETL automation, data enrichment, reporting commentary, and intelligent data distribution across the enterprise.

Design and build AI-powered data interaction capabilities, including natural language-to-data experiences, conversational access layers, and enterprise search over structured and unstructured finance data.

Architect and implement RAG-based solutions that combine enterprise data sources, metadata, business rules, and contextual retrieval to support trusted AI-assisted workflows.

Build and govern LLM orchestration patterns, including prompt design, tool usage, model routing, context grounding, and secure integration with enterprise data systems.