Senior Data Modeler

Credit Acceptance

. Design and maintain logical and physical data models, including dimensional and relational models, across the Databricks lakehouse environment .

Posted 10/30/2026full-timeUnited StatesSenior💰 $136,723 - $191,412 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in data modeling and architecture, particularly with dimensional modeling (Kimball) and relational modeling (3NF). Proficient in SQL and skilled in collaborating with data engineers to implement models in ELT pipelines while adhering to data governance standards.

Highest-signal resume keywords

Dimensional Modeling (Kimball)Relational Modeling (3NF)SQL ProficiencyData Governance ConceptsDatabricks Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ModelingData ArchitectureAnalytics EngineeringELT PipelinesMetadata ManagementData QualitySemantic Layer DesignData Dictionary MaintenanceData CatalogingData Profiling

Soft Skills

Strong Communication SkillsCollaboration Across TeamsSelf-DirectedAutonomy in Architectural Direction

Tools & Technologies

DatabricksER/StudioERwinSqlDBMDbtUnity CatalogDelta LakeAI-Assisted Tooling

Industry Keywords

Financial ServicesAuto LendingRegulated IndustriesKnowledge GraphsOntologiesData Vault Methodology

Tech Stack

Tools & technologies

CloudSQLUnityVault

About the role

Key responsibilities & impact

Design and maintain logical and physical data models, including dimensional and relational models, across the Databricks lakehouse environment
Apply Kimball star schema and normalized modeling best practices
Translate business requirements into documented data structures for analytics, reporting, and AI consumption
Model data across its full lifecycle, from systems of record through integration layers to lakehouse and downstream consumers
Support semantic-layer artifacts such as curated views, conformed dimensions, governed metrics, and Genie-ready configurations
Partner with the Principal Data Engineer to improve metadata practices and machine-interpretable descriptions
Contribute to critical data element identification, business glossary development, and data dictionary maintenance
Explore AI-assisted tooling and programmatic metadata generation to reduce manual cataloging
Partner with data engineers to implement models in performant ELT pipelines and denormalized views
Participate in design reviews and provide modeling guidance during development
Collaborate cross-functionally with business stakeholders and analytics teams
Adhere to and refine enterprise data modeling standards, naming conventions, and design patterns
Support model reviews, change management, and data quality efforts
Identify opportunities to improve modeling approaches and reduce duplication

Requirements

What you’ll need

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 (Databricks or similar)
Awareness of how data models serve downstream AI and analytics use cases, with 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, or dbt
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 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
Required degrees must have been earned at institutions accredited by the Council for Higher Education Accreditation or equivalent
Preferred: Familiarity with metadata management platforms, data catalogs, or data documentation tools (e.g., Collibra, Unity Catalog)
Preferred: Awareness of knowledge graphs, ontologies, or formal data description frameworks (OWL, RDF)
Preferred: Hands-on Databricks experience including Unity Catalog, Delta Lake, and lakehouse architecture patterns
Preferred: Familiarity with Data Vault methodology
Preferred: Experience in financial services, auto lending, or regulated industries
Preferred: Exposure to AI-assisted tooling for metadata, documentation, or data profiling tasks

Benefits

Comp & perks

Annual variable incentive of cash and equity, between 7.5–15% of base salary
401(K) match
Adoption assistance
Parental leave
Tuition reimbursement
Comprehensive medical, dental, and vision benefits
Many nonstandard benefits that make us a Great Place to Work
Hybrid work model with two work-from-home days and three standard in-office days each week