What You’ll Do

• Collaborate with technology architects, Data Product Owners, and engineering teams to implement the business data consumption strategy for SAM’s Snowflake-based data platform, including medallion architecture, data quality expectations, and semantic-layer readiness standards aligned with Schwab’s data and AI strategies.
• Define and operationalize data contracts, including producer and consumer agreements, semantic-layer requirements, schema standards, and freshness service-level agreements.
• Design and evolve a governed semantic layer that enables trusted consumption by business intelligence, Cortex, Cowork, Copilot, and future agentic AI systems.
• Apply data mesh, data fabric, data graph, and knowledge graph concepts in alignment with enterprise data strategy.
• Partner with Technology on Snowflake governance, operating model, and total cost of ownership while helping mitigate platform fragmentation.
• Improve metadata accessibility and prepare SAM’s data estate for governed, semantically rich, agent-consumable workflows.
• Build proofs of concept that demonstrate how metadata, lineage, semantic models, and governance data products can accelerate AI use cases.
• Define and operationalize SAM’s data ownership model, stewardship framework, governance council, business glossary, technical metadata, semantic definitions, data quality, and lineage practices.
• Lead the maintenance of Critical Data Elements with end-to-end lineage and impact analysis in alignment with enterprise governance expectations.
• Enable data consumers, Data Product Owners, technology partners, and external consulting partners through clear standards, trusted data interfaces, and effective cross-functional leadership.
• Lead, develop, and inspire the Data Management and Data Governance teams, establishing clear roles, responsibilities, and operating rhythms.
• Remain current on the evolution of analytics and AI consumption tools, including Alteryx, Denodo, Cortex, and Snowflake Cowork AI, and contribute forward-looking ideas that advance SAM’s data consumption experience.
• The role is intended to evolve beyond traditional data management.
• Strong emphasis on AI readiness and future-state data architecture.
• Candidate should possess asset management domain expertise and understand how data supports investment management functions.
• Hands-on enough to develop examples, prototypes, and demonstrate practical solutions.

Required Qualifications:

• 10 or more years of experience in data management, solution or data architecture, data governance, or data platform roles within financial services
• Work experience in financial asset management or financial investment operations.
• Executive presence and the ability to communicate credibly with senior leaders, data architects, product owners, data consumers, and AI leads.
• Have built AI readiness strategy for future state data architecture
• Multiple instances of building presentations and presenting to executive level leadership
• Demonstrated ability to bridge technical architecture and business strategy.
• Hands-on experience with Snowflake or an equivalent modern cloud data platform, including medallion architecture, data sharing, and governance features.
• Expertise in master data management, data governance frameworks, metadata management, data quality, and data lineage.
• Experience defining and operationalizing data contracts, including producer and consumer agreements and schema standards.
• Understanding of semantic-layer design and how it enables business intelligence, artificial intelligence, and natural-language query systems.
• Proven ability to lead and develop data governance, stewardship, lineage, or quality teams.
• A strong governance mindset balanced with an innovation-first orientation, including experience building proofs of concept and demonstrating value.

Preferred Qualifications:

• Experience with Snowflake Cortex or Cowork, Denodo, Alteryx, or similar analytics and AI consumption tools.
• Familiarity with agentic AI architecture and how governed data estates enable autonomous agent workflows.
• Demonstrated fluency in data mesh, data fabric, and data graph concepts, with the ability to explain how they apply in practice.
• Experience working with enterprise data organizations, such as central CDO or CDAO functions, in a federated governance model.