
Semantic Modeler Job Description Template
This template focuses on the core competencies needed to bridge the gap between complex data and business insights.
Role Summary:
Design and implement enterprise-level semantic layers and dimensional models to enable self-service analytics.
Transform raw data into a coherent, business-ready asset that powers AI/ML accuracy and organizational decision-making.
Key Responsibilities:
Ontology & Taxonomy: Lead the development and governance of business ontologies to ensure consistent data interoperability.
Technical Design: Build and maintain dimensional models (star/snowflake schemas) optimized for high-performance cloud environments like Snowflake or Databricks.
Stakeholder Alignment: Collaborate with business analysts to define clear metrics and conformed dimensions that minimize misinterpretation.
AI Integration: Ground AI systems in business semantics to ensure accurate, trustworthy results for conversational analytics.
Required Qualifications:
Experience: 4 8+ years in data modeling with specific exposure to BI environments and self-service ecosystems.
Technical Skills: Mastery of SQL and proficiency in semantic tools like dbt (MetricFlow), LookML, or Power BI.
Soft Skills: Strong business acumen and the ability to translate complex data sets into clear visual representations for stakeholders.