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!
We understand that your career search may look different than others. Our hiring team wants to make sure that this would be a fit not just for us, but for you long term. If you are actively looking or starting to explore new opportunities, send us your application!
P.S.
We have great details around our stats, success, history and more. We’re proud of our culture and are happy to share why – let’s talk!
Required degrees must have been earned at institutions of Higher Education which are accredited by the Council for Higher Education Accreditation or equivalent.
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.
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