dbt Labs

In 2019, Julia Schottenstein discovered dbt while working as a professional investor at NEA focusing on DevTools and infrastructure companies. She was struck by how users described dbt not as just a tool, but as part of their identity—a level of enthusiasm she'd rarely encountered. The timing was perfect: cloud data warehouses like Snowflake were exploding (growing from $4B to $12B valuation that year), creating massive opportunity for a data transformation standard.

Julia became obsessed and spent months trying to get close to founder Tristan Handy. In 2020, Tristan finally called with news that dbt was raising—but Julia lost the deal to Sequoia. Undeterred, she bet nearly 20% of her liquid net worth on the company's future and eventually asked Tristan if she could join the product team.

What made dbt special wasn't complexity—it was radical simplicity. The founders believed that people doing data analysis should also be able to contribute to creating clean data assets in production, a job traditionally locked behind data engineering expertise. dbt made this possible through SQL, with guardrails that kept data quality high while remaining simple to learn.

Before dbt became a product, Fishtown Analytics ran a consulting business for nearly two years. The founders used this time to solve data transformation problems manually for clients while building dbt in parallel. Every time they hit friction or inefficiency, they baked the solution into dbt. This hands-on customer work was crucial—the product matured through real usage, not speculation.

dbt's distribution advantage came from being open source. The core transformation engine remained free and open, reducing friction to adoption. Users could get started without touching sales. They loved the product, talked about it constantly, and shared it with teammates and other companies.

The flywheel accelerated: lower friction meant more adoption, more diverse use cases across company sizes and industries, which meant a larger user base to attract ecosystem partners building complementary tools. By the time Julia joined, this momentum was undeniable—20,000 companies now use dbt every week.

Two decisions made dbt unstoppable: power through simplicity, and unwavering commitment to being open.

On simplicity: while competitors dismissed dbt as "just a SQL templating tool," that simplicity was the unlock. It democratized data work.

On openness: dbt's open-source core created network effects. It became the standard that everyone standardized on, which attracted partners, which made the ecosystem more valuable, which drove more adoption. The proprietary dbt Cloud offering focused on stateful interactions and cross-team collaboration—the parts that required paid infrastructure.

One pricing lesson Julia shared: most companies wait too long to have willingness-to-pay conversations. dbt finally ran its first pricing change after years, learning that customers valued dbt at 20-35% of what they spent on cloud data warehouses—but dbt only captured a tiny fraction of that value. By design.

dbt Labs became the default standard for the modern data stack. Julia led the acquisition of Transform, a semantic layer company with strong product but no distribution—the inverse of dbt's problem. They positioned themselves as friendly partners even while competing, which made integration post-acquisition seamless.

The company operates on core values: value creation over value capture, radical transparency (sharing board decks openly), humility, and work done well as its own end. These aren't slogans—they're lived through hiring from the community of people who fell in love with dbt and wanted to build it further. That ownership drives obsession with the user experience that most companies can only talk about.