Your OpportunityAt Schwab, you're empowered to make an impact on your career. Here, innovative thought meets creative problem solving, helping us "challenge the status quo" and transform the finance industry together.
We believe in the importance of in-office collaboration and fully intend for the selected candidate for this role to work on site in the specified location or locations.
Schwab Asset Management (SAM) and the Schwab Center for Financial Research (SCFR) deliver trusted, high-quality investment insights that help advisors and clients make informed decisions. Within SAM's Asset Management Infrastructure organization, the Data Foundations & AI Readiness team establishes the trusted, governed, and AI-ready data estate that supports analytics, business intelligence, artificial intelligence, and future agentic capabilities across SAM and SCFR.
As Director, Data Foundations & Enablement, you will lead SAM's AI-ready data foundation and partner across data, analytics, engineering, architecture, and business teams to make trusted, governed, and discoverable data available for analytics, decision-making, Copilots, and future agentic AI capabilities. You will establish the data management, governance, metadata, quality, semantic, and ownership practices that enable scalable AI adoption while leading the teams responsible for defining, stewarding, and continuously improving critical data assets. This role reports to the Managing Director, Head of Asset Management Infrastructure.
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.
What you haveRequired 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.
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