Senior Data Strategy Jobs in Michigan (NOW HIRING)

Credit Acceptance is proud to be an award-winning company recognized both locally and nationally across multiple workplace categories. Our world-class culture is shaped by dedicated team members who are driven to succeed as professionals individually and together as a team. Backed by a strong product, exceptional people, and a stable financial foundation, we've grown into a leading provider of used and new car financing across the country.

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.

Competencies: The following items detail how you will be successful in this role.

• Customer Empathy: Customer Empathy is the ability to understand the perspectives, pain points, and experiences of customers. It involves actively putting oneself in the customer's shoes, comprehending their needs and challenges, and using that understanding to provide a better, more customer-centric experience.
• Engineering Excellence: Engineering Excellence is about bringing great craftsmanship and thought leadership to deliver an outstanding product that delights customers and solves for the business. This involves the pursuit and achievement of high standards, best practices, innovation, and superior solutions.
• One Team: A One Team mindset refers to a collaborative approach across the organization, where individuals work together seamlessly, without boundaries, as a single, cohesive team. Shared goals, open communication and mutual support create a sense of collective purpose. This enables teams to navigate challenges and pursue shared objectives more effectively.
• Owner's Mindset: Owner's Mindset involves adopting a set of behaviors that reflect a sense of responsibility, accountability, strategic thinking, and a proactive approach to managing your domain. As an owner, you understand the business and your domain(s) deeply and solve for the right outcome for the domain(s) and the business.

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 (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

Preferred:

• 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

Target Compensation: A competitive base salary range from $114,000 - $167,000.This position is eligible for an annual variable cash bonus, between 7.5 - 15%.Bonus amounts are based on individual performance. Final compensation within the range is influenced by many factors including role-specific skills, depth and experience level, industry background, relevant education and certifications.

Candidates who reside in the following major metropolitan areas may be eligible for a premium on top of the posted range based on their specific zone: San Francisco, Seattle, Boston, New York City, Los Angeles and San Diego.

INDENGLP

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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

Our Company Values:

To be successful in this role, Team Members need to be:

• Positive by maintaining resiliency and focusing on solutions
• Respectful by collaborating and actively listening
• Insightful by cultivating innovation, accumulating business and role specific knowledge, demonstrating self-awareness and making quality decisions
• Direct by effectively communicating and conveying courage
• Earnest by taking accountability, applying feedback and effectively planning and priority setting

Expectations:

• Remain compliant with our policies processes and legal guidelines
• All other duties as assigned
• Attendance as required by department

Advice!

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