Principle – Knowledge Governance & GenAI Enablement.

Our vision for the future is based on the idea that transforming financial lives starts by giving our people the freedom to transform their own. We have a flexible work environment, and fluid career paths. We not only encourage but celebrate internal mobility. We also recognize the importance of purpose, well-being, and work-life balance. Within Empower and our communities, we work hard to create a welcoming and inclusive environment, and our associates dedicate thousands of hours to volunteering for causes that matter most to them. Chart your own path and grow your career while helping more customers achieve financial freedom. Empower Yourself. Principle – Knowledge Governance & GenAI Enablement. Role Summary – The Principle, Knowledge Governance & GenAI Enablement is accountable for ensuring that enterprise knowledge is structured, governed, and AI ready, enabling reliable analytics, trustworthy GenAI outputs, and governed data products at scale. This role bridges semantic architecture, knowledge lifecycle governance, and GenAI enablement, translating complex knowledge structures into consistent business meaning across platforms including Snowflake, Collibra, and AI Systems. Scope & Authority

• Enterprise-wide semantic ownership across Snowflake, analytics, and GenAI platforms.
• Formal decision rights over enterprise ontologies and semantic standards.
• Partners with engineering and AI Teams to ensure semantic governance standards are met before production deployment.
• Accountable for production knowledge integrity and lifecycle enforcement.

Enterprise Semantic Layer (Snowflake)

• Own the enterprise semantic layer within Snowflake, including business definitions, metric standardization, and governed data products.
• Align ontologies, knowledge graphs, and Snowflake semantic models to prevent metric drift and conflicting business logic.
• Define standards for semantic modeling across analytics, BI, and AI use cases.
• Establish change management, versioning, and approval workflows for semantic updates.

Ontology & Semantic Architecture

• Design, govern, and evolve enterprise ontologies enabling reasoning, explainability, and disambiguation.
• Establish modeling standards for entities, relationships, attributes, and versioning.
• Ensure interoperability across domains, platforms, and AI systems.

GenAI Enablement

• Embed governed knowledge into RAG systems, knowledge graphs, and agent frameworks.
• Define repeatable onboarding processes for new GenAI use cases.
• Establish guardrails for structured vs unstructured knowledge usage.
• Improve grounding, reduce hallucination, and increase answer consistency through semantic strength.

Knowledge Lifecycle & Governance

• Define and institutionalize lifecycle processes: creation, validation, approval, publication, monitoring, and retirement.
• Establish domain ownership and stewardship models for semantic assets.
• Enforce governance for accuracy, provenance, compliance, and auditability.
• Detect and mitigate semantic drift, outdated content, bias, and leakage.
• Serve as escalation authority for knowledge integrity disputes.

Measurement & Impact

• Improve retrieval precision and recall across GenAI use cases.
• Reduce hallucinations through strengthened semantic foundations.
• Decrease semantic inconsistencies across reporting and AI systems.
• Increase AI trust, adoption, and speed to insight.

Qualifications

• Bachelor’s degree in Computer Science or related field (advanced degree preferred).

•Experience with Enterprise data governance platforms (eg. Collibra, Alation, Atlan, etc) Strongly preferred.

• 10 years in knowledge management, data modeling, ontology design, or semantic technologies.
• Demonstrated enterprise-scale ontology or knowledge graph design experience.
• Strong understanding of GenAI architectures, including RAG and agent-based systems.
• Experience in regulated or risk-sensitive environments preferred.
• Executive-level communication skills.

What Success Looks Like

• Snowflake semantic layer becomes the single source of business meaning across analytics and AI.
• Data Owners and Stewards across the business can confidently find, understand, and trust governed knowledge assets in both analytics and AI contexts.
• Metric drift and semantic inconsistencies are materially reduced.
• GenAI systems consistently produce accurate, explainable, context-aware outputs.
• Clear domain ownership exists for all enterprise semantic assets.
• AI adoption accelerates due to increased trust and governance clarity.

We are an equal opportunity employer with a commitment to diversity. All individuals, regardless of personal characteristics, are encouraged to apply. All qualified applicants will receive consideration for employment without regard to age, race, color, national origin, ancestry, sex, sexual orientation, gender, gender identity, gender expression, marital status, pregnancy, religion, physical or mental disability, military or veteran status, genetic information, or any other status protected by applicable state or local law. Apply To This Job