Senior Data Modeler

Credit Acceptance Corporation•San Francisco, CA

6d•Hybrid

About The Position

Credit Acceptance is seeking a Senior Data Modeler to help modernize its data capabilities and build solutions that support analytics, reporting, and AI. The role designs and maintains logical and physical dimensional and relational data models across a Databricks lakehouse environment, applying Kimball star schema and normalized modeling practices. It translates business requirements into documented data structures and considers data’s full lifecycle from systems of record through integration layers to the lakehouse and downstream consumers. The position supports semantic-layer development through curated views, conformed dimensions, governed metrics, and Genie-ready configurations so AI agents and self-service analytics can correctly interpret enterprise data. It also contributes to metadata practices, critical data element identification, business glossaries, data dictionaries, and exploration of AI-assisted or programmatic cataloging. Working with data engineers, business stakeholders, and analytics teams, the Senior Data Modeler helps implement models in performant ELT pipelines and denormalized views. The role participates in design reviews, provides modeling guidance, supports governance and data quality, and helps refine enterprise modeling standards, naming conventions, review processes, and change management. The position follows a hybrid schedule of two work-from-home days and three in-office days each week at a local hub office. Candidates must reside in or be willing to relocate to the greater Southfield, Michigan area; greater Bellevue, Washington area; or San Francisco Bay Area, California.

Requirements

8+ years of experience in data modeling, data architecture, or analytics engineering roles.
Strong hands-on expertise in dimensional modeling (Kimball) and relational modeling (3NF).
Experience modeling data across multiple source systems in cloud data platforms such as Databricks or similar.
Awareness of how data models serve downstream AI and analytics use cases, and willingness to deepen expertise in semantic-layer design and AI-ready data structures.
Proficient SQL skills and experience working closely with data engineers on ELT pipelines.
Experience with data modeling and design tools such as ER/Studio, ERwin, SqlDBM, dbt, or similar.
Understanding of data governance concepts, including lineage, metadata management, and data quality.
Strong communication skills and ability to collaborate across technical and business teams.
Self-directed and comfortable operating with autonomy while working within established architectural direction.
Experience with or exposure to semantic layers, governed metrics, or analytics modeling frameworks.
Must reside in or be willing to relocate to the greater Southfield, Michigan area; greater Bellevue, Washington area; or San Francisco Bay Area, California.

Nice To Haves

Familiarity with metadata management platforms, data catalogs, or data documentation tools such as Collibra or Unity Catalog.
Awareness of knowledge graphs, ontologies, or formal data description frameworks such as OWL and RDF; deep expertise is not required, but curiosity and willingness to learn are essential.
Hands-on Databricks experience, including Unity Catalog, Delta Lake, and lakehouse architecture patterns.
Familiarity with Data Vault methodology.
Experience in financial services, auto lending, or regulated industries.
Exposure to AI-assisted tooling for metadata, documentation, or data profiling tasks.

Responsibilities

Design and maintain logical and physical dimensional and relational data models across the Databricks lakehouse environment.
Apply Kimball star schema and normalized modeling best practices, translating business requirements into documented data structures for analytics, reporting, and AI.
Model data with awareness of its lifecycle across systems of record, integration layers, the lakehouse, and downstream consumers.
Support semantic-layer artifacts, including curated views, conformed dimensions, governed metrics, and Genie-ready configurations.
Partner with the Principal Data Engineer to evolve metadata practices toward machine-interpretable business definitions, relationships, and constraints.
Contribute to critical data element identification, business glossary development, and data dictionary maintenance.
Explore ways to reduce manual cataloging through AI-assisted tooling and programmatic metadata generation.
Partner with data engineers to implement models in performant ELT pipelines and denormalized views, including Dealer Datahub patterns.
Participate in design reviews, provide modeling guidance during development, and work cross-functionally with business stakeholders and analytics teams.
Follow and help refine enterprise data modeling standards, naming conventions, and design patterns within the SDLC.
Support model review processes, change management, and data quality efforts; identify opportunities to improve modeling approaches or reduce duplication.
Remain compliant with company policies, processes, and legal guidelines; perform other duties as assigned and meet department attendance requirements.