[Hiring] Senior Data Modeler @Credit Acceptance

Role Description

The Senior Data Modeler designs and evolves high-quality data models across our modern data platform. This role combines strong hands-on modeling expertise with growing involvement in AI-forward data practices—including semantic layers, enriched metadata, and structured data descriptions that enable AI systems to work with enterprise data effectively.

This position will operate as a key contributor within the Data Engineering team, translating business requirements into well-governed logical and physical models that serve analytics, reporting, and emerging AI use cases. While architectural strategy and enterprise standards are set by the Principal Data Engineer and VP of Data Engineering, it will be a critical partner in executing that vision—bringing strong modeling judgment, cross-system awareness, and a willingness to engage with modern semantic and AI-adjacent concepts.

This is not a narrow, single-project role. This position thinks across the data lifecycle—from systems of record through the lakehouse to downstream consumers—and who can grow into increasing ownership of semantic and AI-ready data design over time.

Outcome and Activities

• This position will work from home; occasional planned travel to an assigned Southfield, Michigan office location may be required. However, this position is permitted to work at a Southfield, Michigan office location if requested by the team member.
• Data Modeling and Design: Design and maintain logical and physical data models (dimensional, relational) across our Databricks lakehouse environment. Apply Kimball star schema and normalized modeling best practices. Translate business requirements into clear, well-documented data structures that serve analytics, reporting, and AI consumption.
• Cross-System Data Lifecycle Awareness: Model data with awareness of the full lifecycle—from systems of record through integration layers to lakehouse and downstream consumers. Ensure models account for how data originates, flows, and is consumed across multiple systems, not just within the big data platform.
• Semantic Layer and AI-Ready Data: Support the development of semantic layer artifacts (curated views, conformed dimensions, governed metrics, Genie-ready configurations) that enable AI agents and self-service analytics to interpret enterprise data correctly. Partner with the Principal Data Engineer to evolve metadata practices toward richer, machine-interpretable descriptions—business definitions, relationships, and constraints.
• Metadata and Business Glossary: Contribute to critical data element identification, business glossary development, and data dictionary maintenance. Help explore approaches to reduce manual cataloging effort through AI-assisted tooling and programmatic metadata generation.
• Collaboration and Delivery: Partner with data engineers to implement models in performant ELT pipelines and denormalized views (such as Dealer Datahub patterns). Participate in design reviews, provide modeling guidance during development, and work cross-functionally with business stakeholders and analytics teams.
• Governance and Standards: Adhere to 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.

Qualifications

• 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 (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

Requirements

• Familiarity with metadata management platforms, data catalogs, or data documentation tools (e.g., Collibra, Unity Catalog)
• Awareness of knowledge graphs, ontologies, or formal data description frameworks (OWL, RDF)—deep expertise not required, but curiosity and willingness to learn is 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

Benefits

• Excellent benefits package that includes 401(K) match, adoption assistance, parental leave, tuition reimbursement, comprehensive medical/dental/vision and many nonstandard benefits that make us a Great Place to Work

Company Description

Credit Acceptance is dedicated to providing a safe and inclusive working environment for all. As part of our Culture of Compliance, we are proud to be an Equal Opportunity Employer and value our culturally diverse workforce. All qualified applicants will receive consideration for employment regardless of the person’s age, race, color, religion, sex, gender, sexual orientation, gender identity, national origin, veteran or disability status, criminal history, or any other legally protected characteristic.