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.

• Lead the development of agent-based AI solutions, including integration with platforms such as Snowflake Cortex / Snowflake agents or equivalent frameworks, to enable intelligent querying, summarization, and workflow execution on top of governed data assets.

• Establish and operationalize evaluation frameworks for AI solutions, including response quality, factual grounding, latency, safety, explainability, and business outcome measurement.

• Ensure AI solutions are designed with strong controls for security, governance, entitlements, auditability, and responsible AI practices, particularly in regulated finance environments.

• Provide technical leadership and mentorship to a high-performing team of data engineers, fostering a culture of innovation, collaboration, and continuous improvement.

• Collaborate with business stakeholders, technology partners, architects, and cross-functional teams to define data and AI strategy, requirements, and deliverables aligned with organizational goals.

• Champion modern SDLC practices, including automated testing, CI/CD, and agile methodologies, to ensure high-quality, scalable, and maintainable data and AI solutions.

• Drive automation, data quality, observability, and engineering best practices across all data engineering and AI-enabled solutions.

• Manage stakeholder relationships, communicate project status, and proactively address risks, dependencies, and challenges.

• Champion the adoption of emerging technologies and methodologies to enhance data capabilities, AI enablement, and business value.

What you’ll bring to the role

Required Skills

• 10+ years of experience in data engineering, data architecture, or related roles, with a proven track record of delivering enterprise-level solutions.

• At least 6 years’ relevant experience would generally be expected to find the skills required for this role.

• Deep expertise in SQL, data modeling, ETL, and building scalable data pipelines.

• Strong hands-on experience with cloud data platforms, preferably Snowflake, and modern data engineering tools.

• Mandatory: Strong domain and functional knowledge in finance, investment banking, or related industries.

• Demonstrated experience designing and implementing AI / GenAI solutions for enterprise data and analytics use cases, including RAG architectures, LLM integrations, prompt engineering, model orchestration, and retrieval pipelines.

• Experience building natural language interfaces over enterprise data, enabling business users to interact with governed data systems using AI-assisted experiences.

• Hands-on experience integrating AI capabilities with enterprise data platforms, including Snowflake Cortex / agentic AI patterns / AI-assisted query or workflow frameworks.

• Experience defining and operationalizing evaluation and testing frameworks for LLM-based applications, including quality benchmarking, hallucination detection, grounding validation, and business acceptance criteria.

• Strong understanding of AI governance, model risk, security, data privacy, and responsible AI controls in enterprise environments.

• Demonstrated experience leveraging GenAI, Copilot, or AI-based engineering tools for design, development, code completion, code review, documentation, and automated test case generation.

• Proven ability to lead, motivate, and develop high-performing teams.

• Excellent problem-solving, analytical, and communication skills.

• Experience managing stakeholder relationships and delivering complex projects in a global environment.

• Strong understanding of modern SDLC, agile delivery, and innovation in data engineering and AI engineering.

• Experience with automated testing, testing frameworks, and production-grade release practices.

Additional Skills (Good to Have)

• Familiarity with Power BI, Apache Airflow, and OLAP tools.

• Experience with Python, Shell scripting, workflow automation, and AI application development frameworks.

• Exposure to vector search, embeddings, semantic retrieval, knowledge grounding, and metadata-driven AI architectures.

• Experience with agent frameworks, orchestration tooling, or conversational AI platforms used in enterprise settings.

• Exposure to regulatory and financial reporting requirements.

• Demonstrated track record of driving innovation and GenAI adoption in data engineering projects.

• Passion for continuous learning, business impact, and solution-oriented leadership.