Ramp drives 25% increase in new business with dbt

This is the story of how Ramp built a new sales lead source with Snowflake, dbt, and Hightouch



in data platform costs



in data transformation speed


of sales pipeline

generated by their new personalization engine

About Ramp

Ramp is the first and only finance automation platform and corporate card designed to help businesses spend less time and money. With over 1,000 integrations with financial products like Netsuite and Quickbooks, Ramp makes it easy to issue corporate charge cards, make bill payments, automate accounting processes, and monitor expenses.

Ramp powers $5B+ in payment volume across corporate and small business card and bill payment data, serving 10,000+ businesses and 200,000+ cardholders. Its customer mix is diverse—ranging from small businesses to venture-backed startups, mid-market, and public enterprise players. 

Businesses spend an average of 3.5% less and close their books 8x faster by switching to Ramp. Founded in 2019, Ramp powers America’s fastest-growing corporate card and bill payment software. 

The challenge of exponential growth

Ramp has seen exponential growth since its start, increasing revenue and cardholders by 10x and 15x, respectively, year-over-year in 2021. Ramp’s recent fundraising round, backed by Goldman Sachs, Citi, Founders Fund, Stripe, Coatue, and Thrive, valued the company at $8.1B.

Over the last 12 months, Ramp’s data team has tripled in size to keep pace with the company’s rapid growth and business demands. This influx of new users created concurrency issues—resulting in costly, hard-to-debug failures, forcing the data team to spend too much time managing workloads.

Whenever too many users tried to access a specific database, Ramp’s entire database would deadlock, and nobody could access the data. Scaling up to meet the demands of the various business teams and internal stakeholders took a lot of work. 

“We started to see a lot of issues when it came to scaling our previous data warehouse. We were spending too much time fine-tuning our workloads just so we could keep everything up and running,” explained Kevin Chao, Senior Analytics Engineer at Ramp.

Ramp’s data team also wanted to give their business users access to the data they cared most about in the tools they use every day. This objective, mixed with the challenges of Ramp’s previous data warehouse, led the company to adopt a new data stack that could not only scale with Ramp’s data team but also provide a way for business teams to access the data sitting in the analytics layer.

Ramp’s new data stack

In search of a data stack that could facilitate these needs, Ramp quickly turned to Snowflake, dbt, and Hightouch.

Ramp's data stack


Looking for a scalable and flexible solution that could act as a single source of truth, Ramp landed on Snowflake. As a fully managed data platform, the Data Cloud eliminates all of the bottlenecks that Ramp faced previously. 

Snowflake automatically manages all of the underlying maintenance in the background, allowing the data team to focus their time on transforming the data and building models that can positively impact Ramp’s bottom line.

Snowflake scales automatically as Ramp grows, and the data team no longer has to worry about key tables getting locked or dashboards freezing. With Snowflake, Ramp’s data can automate all of the manual tasks that the admins were forced to manage in the previous platform.

“Snowflake essentially handles everything out of the box without us having to think about anything so we can focus on modeling our data and extracting value,” said Kevin.

Since adopting Snowflake, Ramp can handle more workloads faster. Transformation jobs in Snowflake now complete 33 percent more quickly, and Ramp has seen a 20 percent decrease in overall cost compared to its previous data platform. In addition to this, the team no longer has to worry about contentious deadlocks thanks to Snowflake’s near-unlimited concurrency.


Ramp collects valuable customer data from an array of different data sources, including Postgres, Salesforce, Hubspot, Zendesk, Outreach, and various ad platforms. Spinning up additional software infrastructure to clean and transform this data in the analytics warehouse was difficult. The team built new data models manually without testing or version control, resulting in variations of core metrics.

Ultimately, this led the data team to adopt dbt as their data transformation tool of choice. Rather than building and maintaining ad hoc transformation jobs, Ramp uses dbt to standardize around a core set of metrics and fully automate, schedule, and run every transformation job.

“We can use dbt as a testing ground before fully committing ourselves to a new data product. dbt lets us abstract all of the nitty-gritty details that come with building data models so we can focus on delivering value to our business teams.”

Meeting the right individuals with the right message at the right time is vital to Ramp’s success. With dbt jobs running in Snowflake, Ramp’s data team can aggregate, transform, and enrich all of their customer data in Snowflake to build a complete 360-degree view of the customer. This data is then used to create risk profiles for specific users and improve personalization, whether it’s in the app, on the website, or through automated marketing campaigns.

“All of our models are born and bred in dbt. When people think of clean data, they think of dbt models. There’s very little that isn’t powered by dbt at Ramp,” emphasized Kevin.


Building data models in dbt is one thing, but activating them in downstream sales and marketing channels is another. Ramp’s data team set it sights on enriching Salesforce with all the valuable dbt models living in Snowflake.

Anytime Ramp wanted to move data out of Snowflake, they were forced manually to build and replace various python scripts. This was a challenge because Ramp’s business teams wanted access to this data in Salesforce and Hubspot to improve personalization.

In search of a scalable solution that could solve this problem, Ramp turned to Hightouch. Since adopting Hightouch for Reverse ETL, Ramp has created an entirely new outbound automation team (OATs) which now drives 25 percent of all sales pipeline. Staffed by data engineers, this team collaborates closely with Ramp’s marketers to identify target prospects and deliver customized emails at scale to the right person at the right time. OATs has become the single lowest-costing customer acquisition channel for Ramp.

“Having enriched data available in Salesforce means the sales team has one view with everything they need to understand what’s going on in their pipeline,” said Kevin.

Ramp also uses Hightouch to enrich Hubspot and Outreach with relevant customer metadata, key events, product usage data, and other sales-related information. Before Hightouch, A/B testing various campaigns was a nightmare. 

Using Hightouch, Ramp can sync custom audiences directly to various ad platforms helping the marketing team optimize return on ad spend (ROAS) and increase conversions from paid ads. Ramp also leverages Hightouch to automate the company’s entire underwriting and application process by syncing data directly to Postgres.

“Thanks to Hightouch, we’re able to use Snowflake for all the heavy computations, allowing our production databases to focus on operations,” said Kevin.

Syncing data to Slack is another major use case for Ramp. With Hightouch, Ramp’s data team is notified about potential problems in their data stack before they escalate.

“We have very aggressive SLAs for data freshness, and we want to know when something goes wrong in our warehouse. With Hightouch, we get notified immediately.”

What’s Next

Since adopting a modern data stack, Ramp can go from ideation, to validation, to iteration, and set up fully functional operational workflows and marketing campaigns in less than a day. 

“With Hightouch, Snowflake, and dbt, we can go from zero to one as fast as possible,” affirmed Kevin.

For Ramp, the future is continuing to find ways to help businesses become better, more profitable versions of themselves through finance automation that maximizes the output of every dollar and hour. They’re looking to build out their automation platform to reach customers across every expense, payment, purchase, application, and insight (from reporting to forecasting).