The client is a US-based financial technology company that helps automotive, powersports, and RV dealerships close more business by giving their customers fast, accessible financing options. Whether it’s a repair, an accessory, a service contract, or a vehicle purchase, their digital lending products make it easy for dealerships to say yes when customers need it most.
Based in Bellevue, Washington, they operate nationwide, and as their dealer network grew, so did the complexity behind the scenes. With more partners, more loan products, and more data flowing in from every direction, their existing data infrastructure wasn’t keeping up. They needed a scalable analytics ecosystem and to build a foundation that could actually grow with them.
The Challenges
Rapid growth has a way of exposing the gaps in a data setup that worked fine at a smaller scale. For the client, as it expanded its lending and dealership partner ecosystem, it faced several operational and data challenges:
- Fragmented partner data: Every dealer network, vendor, and external partner was sending data in its own format. Onboarding new partners was slow, and getting consistent reporting across all of them was even harder.
- Growing loan volumes: A fast-expanding portfolio of installment loans and revolving credit products meant transaction data was piling up faster than the existing infrastructure could process it reliably.
- Limited real-time visibility: Business teams needed timely access to metrics that mattered, such as loan originations, approvals, and declines, dealer performance, delinquencies, charge-offs, and portfolio utilization.
- Manual reporting overhead: Existing reporting workflows required constant maintenance and slowed down decision-making at exactly the moments when speed counted.
- Data warehouse strain: As analytics needs grew, the data warehouse required ongoing optimization and support that the internal team couldn’t sustainably absorb on its own.
How We Helped
Our work covered three interconnected areas, i.e., data engineering, warehouse development, and business intelligence. Each reinforces the other to give the client a platform they could genuinely rely on, ensured by our solutions:
- Partner & data integration: Integrated external dealer networks, vendors, and partners directly into the enterprise data warehouse, standardizing inbound data from multiple sources into clean, trusted analytical models.
- Automated pipelines: Built end-to-end ETL/ELT pipelines using dbt, AWS Step Functions, and AWS Lambda, replacing manual workflows with automated, reliable data flow.
- Data warehouse development: Enhanced the underlying architecture for performance and scalability, and built dedicated data marts covering lending, servicing, collections, and dealer analytics.
- Ongoing production support: Provided continuous warehouse support and issue resolution, keeping the platform stable and responsive as demands on it kept growing.
- Tableau dashboards: Designed and enhanced reporting across executive portfolio views, dealer performance, loan processing KPIs, delinquency and charge-off monitoring, and collections trends.
The Results
The platform the client operates on today looks and behaves very differently from what they started with. Reporting that used to lag now refreshes on real-time cycles. Partner onboarding that was once slow and inconsistent has become a repeatable, automated process. Leadership teams have the portfolio visibility they need to make decisions with confidence rather than working off delayed snapshots.
What made the difference was having a team that understood both the data engineering and the business context behind it. Over time, that combination built something harder to replicate than any pipeline: a working relationship where the client trusted us not just to execute, but to think ahead on their behalf. The engagement has continued to deepen as the platform has grown, with both sides invested in making it better.
