Vice President – Lead Data Engineering & AI

Profile Description

Were seeking someone to join ourFRPPE Techteam as Lead Data Engineering & AI in Finance Technology to lead the design development and implementation ofenterprise-scale data warehouse reporting analytics and AI-enabled data solutions preferably on cloud platforms such asSnowflake.

This role requires a strong leader with a blend ofdeep data engineering expertiseandapplied AI / GenAI architecture experienceto build intelligent scalable solutions across finance data platforms. The ideal candidate will help shape the next generation of data products by enablingnatural language interaction with enterprise dataretrieval-augmented generation (RAG)LLM orchestrationagent-based workflows andevaluation frameworksfor 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 leveragingGenAI for finance productivitybuilding 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 aLead Data Engineering & AI role within the job family responsible for developing and maintaining software solutions that support business needs with an expanded mandate to driveAI-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 youll do in the role

Responsibilities

• Lead the architecture design and implementation ofenterprise-scale data platforms including data warehousing semantic modeling reporting analytics and data distribution solutions.

• Drive the adoption and integration ofGenAI LLMs and modern AI/ML techniquesfor ETL automation data enrichment reporting commentary and intelligent data distribution across the enterprise.

• Design and buildAI-powered data interaction capabilities includingnatural language-to-data experiences conversational access layers and enterprise search over structured and unstructured finance data.

• Architect and implementRAG-based solutionsthat combine enterprise data sources metadata business rules and contextual retrieval to support trusted AI-assisted workflows.

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

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

• Establish and operationalizeevaluation frameworksfor AI solutions including response quality factual grounding latency safety explainability and business outcome measurement.

• Ensure AI solutions are designed with strong controls forsecurity 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 definedata and AI strategy requirements and deliverables aligned with organizational goals.

• Champion modernSDLC 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 youll bring to the role

Required Skills

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

• At least6 years relevant experiencewould generally be expected to find the skills required for this role.

• Deep expertise inSQL data modeling ETL and building scalable data pipelines.

• Strong hands-on experience withcloud data platforms preferablySnowflake and modern data engineering tools.

• Mandatory: Strong domain and functional knowledge infinance investment banking or related industries.

• Demonstrated experience designing and implementingAI / GenAI solutions for enterprise data and analytics use cases includingRAG architectures LLM integrations prompt engineering model orchestration and retrieval pipelines.

• Experience buildingnatural 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 includingSnowflake Cortex / agentic AI patterns / AI-assisted query or workflow frameworks.

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

• Strong understanding ofAI governance model risk security data privacy and responsible AI controlsin enterprise environments.

• Demonstrated experience leveragingGenAI Copilot or AI-based engineering toolsfor 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 modernSDLC agile delivery and innovation in data engineering and AI engineering.

• Experience withautomated testing testing frameworks and production-grade release practices.

Additional Skills (Good to Have)

• Familiarity withPower BI Apache Airflow and OLAP tools.

• Experience withPython Shell scripting workflow automation and AI application development frameworks.

• Exposure tovector search embeddings semantic retrieval knowledge grounding and metadata-driven AI architectures.

• Experience withagent frameworks orchestration tooling or conversational AI platformsused in enterprise settings.

• Exposure toregulatory and financial reporting requirements.

• Demonstrated track record of drivinginnovation and GenAI adoptionin data engineering projects.

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