- Define enterprise modeling standards across staging, intermediate, dimensional, fact, semantic, and mart layers.
- Establish conventions for model grain, naming, ownership, lineage, and documentation, and lead architectural design reviews.
- Design and implement reusable business entities, semantic models, and enterprise metrics.
- Set standards for dbt development, testing, Git workflows, CI/CD, deployment reliability, model performance, and observability.
- Design governed access patterns for applications, AI agents, and self-service tools consuming Snowflake data.
- Partner with Data Engineering, BI & Analytics, Revenue Operations, Finance, and executive stakeholders to translate business requirements into data models and analytics solutions.
- Guide analytics engineers on modular architecture, semantic implementation, and reusable modeling patterns.
- Evaluate tools, semantic technologies, governance capabilities, and architectural improvements.
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