Role Description We are seeking an experienced Senior Data Governance Lead to drive the next phase of our data governance evolution. This role is ideal for a hands-on leader who can translate abstract governance concepts into clear taxonomies, measurable data rules, and enforceable quality standards that scale across the organization. You will help to lead a taxonomy and rules transformation—establishing shared definitions, structured classification systems, and outcome-driven data quality rules that directly improve data trust, usability, and business impact. This role partners closely with data, product, engineering, operations, and analytics teams to embed governance into how data is designed, produced, and consumed—not as overhead, but as enablement. This role is critical to building long-term data governance muscle across the organization by equipping teams with practical frameworks, guidance, and repeatable ways of working. Responsibilities: - Lead the design and governance of an enterprise taxonomy and data rule framework, including business terms, entities, products, and classification hierarchies that support consistent data use. - Translate business needs into clear, objective, and measurable data and quality rules that can be implemented, tested, and monitored. - Develop governance frameworks that connect taxonomy → business rules → data quality standards → measurable outcomes. - Partner with data engineering, analytics, and product teams to embed governance requirements into data pipelines, tooling, and workflows. - Establish standards for rule authoring, validation, conflict detection, and lifecycle management. - Drive clarity and consistency in definitions, metadata, and documentation across domains. - Support the creation of data quality metrics, thresholds, and reporting aligned to business use cases. - Coach and enable data owners, stewards, analysts, and associates to apply governance standards confidently and consistently. - Contribute to governance operating models, playbooks, and training materials designed for both technical and non-technical audiences. - Act as a thought partner on governance best practices, tooling needs, and maturity progression. Success Measures: - Taxonomy Adoption & Clarity – Demonstrated reduction in ambiguity and rework through consistent use of approved taxonomies and definitions. - Rule Effectiveness – Clear, testable data and quality rules that lead to measurable improvements in data accuracy, consistency, and trust. - Scalability of Governance – Frameworks and standards that teams can apply independently without constant governance intervention. - Data Quality Improvements – Observable improvements in key data quality indicators tied to governed datasets. - Stakeholder Enablement – Positive feedback from teams on the usability, clarity, and practicality of governance guidance. - Operationalization – Governance artifacts (taxonomy, rules, standards) actively used in production workflows rather than existing only as documentation. Qualifications - 5–10 years of experience in data governance, data management, data quality, analytics, or data operations. - Proven experience designing or implementing taxonomy frameworks, classification systems, or business term definitions. - Strong ability to translate complex business concepts into structured, objective rules. - Experience working cross-functionally with data engineering, analytics, product, and operational teams. - Demonstrated success moving organizations from ad hoc or narrative governance toward structured, standards-based approaches. - Familiarity with data quality dimensions (e.g., accuracy, completeness, timeliness, consistency) and how to operationalize them. - Knowledge of or certification in DAMA / DMBOK is a strong plus. - Comfort operating in environments with evolving maturity and ambiguity. - Excellent written and verbal communication skills.

• Design, build, and deploy LLM-based solutions on Snowflake, leveraging Cortex AI functions (e.g., Snowflake LLM inference, fine-tuning, embeddings). • Create custom LLM pipelines using Snowpark ML and external models integrated through Snowflake. • Build and operationalize machine learning models, including NLP, predictive modeling, generative AI, and recommendation systems. • Develop and apply prompt engineering, RAG pipelines, and semantic search patterns using Snowflake features and Vector Embeddings. • Conduct exploratory data analysis (EDA), feature engineering, and statistical modeling using Snowflake & Snowpark Python. • Deploy, monitor, and optimize model performance directly within the Snowflake environment. • Integrate Snowflake ML solutions with cloud platforms (GCP) and downstream applications. • Work with business stakeholders, product teams, and analytics leaders to identify opportunities for AI-driven transformation. • Deliver high-quality, interpretable insights and communicate complex technical results to non-technical audiences. • Partner with data engineers, architects, and analysts to build end-to-end Snowflake-native AI solutions.

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• Design and implement AI features in strategic products. • Build LLM-based solutions for use cases such as customer support, triage, recommendation, information generation/extraction, and workflow automation. • Develop and orchestrate multi-agent architectures for complex tasks. • Contribute hands-on to experimentation, architectural decisions, and the design of ML and AI solutions for the People BU's products. • Create AI services and production pipelines (APIs, observability, logging, performance monitoring). • Define and maintain quality metrics, conduct experimentation (A/B or controlled tests), and drive continuous improvement. • Help ensure security, privacy, and compliance in the use of sensitive data, including best practices for anonymization and governance. • Document technical decisions, trade-offs, and patterns to help scale AI adoption across the People BU.