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Senior Data Modeler
Credit Acceptance. Design and maintain logical and physical data models, including dimensional and relational models, across the Databricks lakehouse environment .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive expertise in data modeling and architecture, particularly in dimensional modeling (Kimball) and relational modeling (3NF), with proficiency in SQL and experience in cloud data platforms like Databricks. Capable of translating business requirements into effective data structures while ensuring adherence to data governance and quality standards.
Highest-signal resume keywords
Dimensional Modeling (Kimball)Relational Modeling (3NF)SQL ProficiencyDatabricks ExperienceData Governance Concepts
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data ModelingData ArchitectureAnalytics EngineeringELT PipelinesData Quality InitiativesSemantic Layer DesignMetadata ManagementData Dictionary MaintenanceData CatalogingData Vault Methodology
Soft Skills
Strong Communication SkillsCollaboration Across TeamsSelf-DirectedAutonomy in Work
Tools & Technologies
DatabricksER/StudioERwinSqlDBMDbtUnity CatalogDelta LakeAI-Assisted Tooling
Industry Keywords
Financial ServicesAuto LendingRegulated IndustriesKnowledge GraphsOntologies
Tech Stack
Tools & technologiesCloudSQLUnityVault
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 clear, documented data structures for analytics, reporting, and AI consumption
- Model data across its full lifecycle, from systems of record through integration layers to 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 improve metadata practices and machine-interpretable data descriptions
- Contribute to critical data element identification, business glossary development, and data dictionary maintenance
- Explore AI-assisted and programmatic approaches 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 initiatives
- Identify opportunities to improve modeling approaches or 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, 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
- 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
- Required degrees must have been earned at institutions of Higher Education 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
- Hybrid work model with two work-from-home days per week