Sr. Data Modeler

We're looking for a Sr. Data Modeler to design and govern the data models that serve as the foundation for analytics, reporting, and business intelligence across the organization — with particular depth in semantic and metrics-layer modeling. Beyond traditional schema design, you'll build the semantic layer that sits between raw data and downstream consumers (dashboards, reverse-ETL syncs, AI-assisted querying), working closely with data engineers, analysts, and business stakeholders to keep definitions consistent everywhere they're used.
What you'll get to do:
Contribute to the semantic layer strategy — defining metrics, dimensions, and business logic once so they're consistent across BI tools, reverse-ETL syncs, and AI-assisted querying
Design serving-layer models as the "source of truth" for business definitions, resolving ambiguity before it reaches dashboards
Build and govern semantic views/metrics layers (e.g., Snowflake semantic views, dbt metrics/semantic layer) that let analysts and AI tools query curated data without re-deriving business logic
Partner directly with analytics and BI consumers to understand how models get used downstream, and iterate on semantic definitions based on real query patterns
Reduce metric drift by auditing existing reports/dashboards for divergent definitions of the same business concept and consolidating them into governed semantic models
Who you are:
5+ years of experience in data modeling or a related field, including experience operating at a senior or technical lead level
Experience designing or maintaining a semantic layer or metrics layer (e.g., dbt Semantic Layer, LookML, Snowflake semantic views, Cube) that serves multiple downstream consumers
Strong point of view on the difference between a data model and a semantic model, and how to design for both correctness and analyst usability
Advanced SQL skills and deep understanding of relational database design
Experience modeling data within modern cloud data warehouses (e.g., Snowflake, BigQuery, Redshift)
Experience working with transformation frameworks such as dbt
Strong understanding of data governance, metadata management, and documentation practices
Excellent communication skills, with the ability to translate business needs into technical designs and explain technical concepts to non-technical stakeholders
Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent practical experience preferred.
Foundational background in dimensional modeling (Kimball and/or Inmon) — helpful context, though this role emphasizes semantic/metrics layer design over traditional schema work
Experience with data catalog or governance tools (e.g., Collibra, Alation, Atlan)
Familiarity with data mesh or data product concepts
Background in a specific industry domain relevant to the business (e.g., finance, healthcare, retail)

Employment type
Full-time

Work arrangement
Yes