At Credit Acceptance, we are building on the foundation of a successful business while investing in the future through technology, data, AI, and continuous improvement. As we continue to evolve, our Team Members play an active role in improving experiences, driving meaningful change, and shaping the future of our business. We foster an environment where people are empowered to contribute ideas, develop their careers, make an impact, and help lead our continued transformation.
Our Engineering teams are helping shape the future of Credit Acceptance through innovation. Team Members collaborate across the organization to solve complex challenges, build impactful solutions, challenge the status quo, and support informed business decisions. We value curiosity, continuous learning, and new ideas from people who are passionate about driving modernization and meaningful business impact.
Outcome and Activities
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…