Sr. Data Modeler

Vuori•Carlsbad, CA

About The Position

Vuori is seeking a Sr. Data Modeler to design and govern data models that support analytics, reporting, and business intelligence across the organization, with particular emphasis on semantic and metrics-layer modeling. The role will build the semantic layer between raw data and downstream consumers, including dashboards, reverse-ETL syncs, and AI-assisted querying. The modeler will work closely with data engineers, analysts, and business stakeholders to ensure business definitions remain consistent wherever they are used.

Requirements

At least 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 or metrics layer serving multiple downstream consumers, such as dbt Semantic Layer, LookML, Snowflake semantic views, or Cube.
A strong point of view on the difference between a data model and a semantic model, and how to design for correctness and analyst usability.
Advanced SQL skills and a deep understanding of relational database design.
Experience modeling data in modern cloud data warehouses such as Snowflake, BigQuery, or Redshift.
Experience with transformation frameworks such as dbt.
Strong understanding of data governance, metadata management, and documentation practices.
Excellent communication skills, including the ability to translate business needs into technical designs and explain technical concepts to non-technical stakeholders.

Nice To Haves

Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent practical experience; listed as preferred.
Foundational background in dimensional modeling, such as Kimball and/or Inmon.
Experience with data catalog or governance tools such as Collibra, Alation, or Atlan.
Familiarity with data mesh or data product concepts.
Background in an industry domain relevant to the business, such as finance, healthcare, or retail.

Responsibilities

Contribute to semantic-layer strategy by defining metrics, dimensions, and business logic consistently across BI tools, reverse-ETL syncs, and AI-assisted querying.
Design serving-layer models as the source of truth for business definitions and resolve ambiguity before it reaches dashboards.
Build and govern semantic views and metrics layers, such as Snowflake semantic views or dbt metrics/semantic layer, so analysts and AI tools can query curated data without re-deriving business logic.
Partner with analytics and BI consumers to understand downstream model use and iterate on semantic definitions based on real query patterns.
Audit existing reports and dashboards for divergent definitions of the same business concept and consolidate them into governed semantic models to reduce metric drift.