Serving diverse analytics needs with a lean data team
Founded in 2021, Bilt Rewards is a platform that lets members earn points through rent payments, neighborhood dining, and travel. Bilt faced the challenge of managing massive datasets while delivering reliable, tailored insights to partners (merchants, property owners, and more), members, and their internal teams. With a lean data team of just three analysts and over 10,000 external data consumers—primarily B2B partners who depend on accurate reporting—Bilt needed a scalable, cost-effective solution to streamline their analytics for every data consumer.
A complex data infrastructure with billion rows
Bilt’s data infrastructure is vast and intricate. Hosted in BigQuery with over 200 schemas, hundreds of billions of rows, ingested from 50+ application databases—from financial partners (Mastercard, Wells Fargo) to property managers, to merchants, and to travel partners. The company has hundreds of different transaction logs, with changing dimensions and various join patterns.
Data serves three main types of data consumers: internal teams, business partners, and members:
- Internal teams: Employees of Bilt who leverage data (via Reverse ETL & BI tools) to deliver personalized user experiences and improve business performance across CRM, website, app, and paid campaigns.
- Members: Those using Bilt to pay for their Home, Neighborhood, and Travels purchase and earn best in class rewards. Members can access their past usage and rewards through Bilt’s mobile app and website, as well as personalized benefit recommendations.
- Business partners: Financial institutions, property managers, merchants, and other partners who access critical data on Bilt’s B2B portals, where trust and accuracy are paramount.
Growing pains across data operations and costs
Although the Bilt team was doing several data transformations for multiple use cases, they had not yet invested in data engineering best practices across the analytics development lifecycle (ADLC)—such as documentation, data lineage, version control, CI/CD, and automated testing. With only transformation in place, these gaps left the team vulnerable to inefficiencies, errors, and eroding trust with both internal and external stakeholders.
Rising analytics costs and lack of personalization for 10,000+ business partners
Bilt initially used a BI embed to deliver data reports and charts to their business partners. While the solution served the initial use case, the per-user pricing model quickly became unsustainable as costs scaled linearly with each new merchant or partner onboarded. Additionally, this pattern limited the types of visualizations Bilt could offer, further constraining their ability to meet partner expectations. Last, data transformations lived in both the data warehouse and in the BI tool causing troubleshooting headaches. For a rapidly growing company, this approach was not viable.
Implementing dbt Cloud to increase the velocity of a lean data team
Managing such a large and diverse data ecosystem as Bilt’s required an efficient and scalable solution. Some data team members had been long-term dbt users before joining the company and identified the tool as a solution for their DataOps problems.
Bilt Rewards initially considered dbt Core but opted for dbt Cloud to meet their speed and scalability needs. dbt Cloud provided tools to centralize business logic, streamline transformations, and implement practices like documentation, lineage tracking, and governance. These out-of-box features were essential for their lean data team of just three individuals. Together, this created a transparent and reliable analytics framework, setting the stage for broader use cases and sustainable growth.
Cost savings and improved data quality
Simplifying data transformation with Semantic Layer
Bilt implemented the dbt Semantic Layer to centralize their metrics and dimensions in the same place as all of their data transformations in dbt Cloud. This allowed them to efficiently deliver data for key use cases like embedded analytics for their B2B partner portals and deliver personalized member experiences on their mobile app and website.
“What makes us most excited about Semantic Layer is that we only need to define metrics once,” said Ben Kramer, Senior Director of Analytics at Bilt Rewards. “We don’t need to define relationships and definitions in all of our downstream tools. We write it once, store it in GitHub, and now we have a true and trusted model we can use everywhere”.
80% costs saved and improved user experience from switching to dbt Semantic Layer
By moving transformation logic from their BI tool into dbt, Bilt transitioned off their BI Embed solution, significantly reducing costs tied to its per-user pricing model. The dbt Semantic Layer decouples business logic—metrics, dimensions, and calculations—from front-end visualizations, enabling a more cost-efficient, headless BI approach through dbt’s GraphQL endpoint.
The shift to the dbt Semantic Layer lowered query costs and streamlined the delivery of accurate and customizable data visualization.
“All we really needed to leverage for our customer-facing visualizations in our B2B portal was the data. Once we centralized data transformations with dbt and their Semantic Layer, we could easily create the visualizations to our front end,” said Ben. “It became super simple. We migrated quickly to sending data via the graphQL endpoint, and our data costs decreased significantly by 80%.”
Superior data quality with DataOps best practices
Standardizing metric transformation and entity relationships in the Semantic Layer was one of the ways dbt Cloud helped Bilt improve data governance and efficiency across their data ecosystem. It built trust with Bilt’s partners and encouraged them to develop new campaigns and complementary offerings.
“We had a lot of data quality issues, but dbt Cloud really solved most of them. And for the hardest problems, the dbt team collaborates with us to create solutions,” said James Dorado, VP of Data Analytics at Bilt Rewards.
Leveraging Semantic Layer for AI and ML use cases
The B2B portal was just the beginning use case for the dbt Semantic Layer at Bilt Rewards. They’re now exploring how to use semantic models to ensure consistent and reliable metrics power their Machine Learning and AI LLM initiatives:
“Our B2B portal demonstrated one powerful way to leverage the Semantic Layer,” said Ben. “It helped us model our business in a way that’s easy for both end users and B2B partners to understand while querying efficiently. It was an excellent starting point for our Semantic Layer journey with AI and BI as our next steps!”
