Companies that use DBT

Source: Analysis of 100 job postings that mention DBT

My analysis shows that DBT is primarily purchased by senior data leaders, with Directors of Data representing 18% of roles, Heads of Data at 15%, and Directors of Analytics at 10%. These executives are responsible for building modern data infrastructure from the ground up. I noticed phrases like "building on a green field from scratch" and "transforming fragmented insights into a cohesive, revenue-driving capability" appearing throughout multiple postings. These buyers prioritize scalability, governance, and enabling self-service analytics across their organizations.

The hands-on users are Data Engineers (12% of roles) and Analytics Engineers who build and maintain the data pipelines daily. They work extensively with SQL and Python, creating transformation layers, dimensional models, and ensuring data quality. I found consistent mentions of responsibilities like "develop transformation layers using dbt" and "implement dbt transformations, data contracts, and automated testing." These practitioners partner closely with product, engineering, and business teams to translate requirements into scalable data solutions.

The pain points center on modernization and trust. Companies repeatedly mention "modernizing legacy data stacks," "ensuring data is a strategic enabler for growth," and building "trusted, reliable data foundations." One posting emphasized the need to "instill a culture of quality throughout the engineering organization, through process, tooling, and standards, with the aim of building trust in our data." Another highlighted "turning complex data into production-grade insights that drive business outcomes." These organizations want to move from manual, fragmented processes to automated, governed systems that accelerate decision-making.

Source: Analysis of tech stacks from 232 companies that use DBT

I noticed that companies using DBT tend to be data-mature, growth-stage organizations that have invested heavily in both their people operations and their infrastructure. The combination of Culture Amp and Qualtrics alongside Lacework and DBT itself tells me these are companies that care deeply about measurement, whether that's employee engagement, customer feedback, or data pipeline performance. They're building sophisticated internal systems and need tools that scale with complexity.

The pairing of DBT with Culture Amp is particularly revealing. Companies don't invest in employee feedback platforms unless they're past 100+ employees and actively managing culture at scale. When you add Workday to the mix, you're looking at organizations with mature HR operations that likely have dedicated People Analytics teams. These teams probably use DBT to transform their HR data into actionable insights about retention, performance, and hiring patterns.

The security angle is equally telling. Lacework appearing so frequently suggests these companies have moved to cloud infrastructure and need enterprise-grade security monitoring. They're not startups cobbling together basic tools anymore. They're at a stage where compliance, data governance, and security posture matter to customers and investors. DBT fits perfectly here because it provides the documentation, testing, and lineage tracking that security and compliance teams demand.

Source: Analysis of Linkedin bios of 232 companies that use DBT

I noticed that DBT users span a remarkably wide range of industries, but they share a common thread: they're operating at serious scale with complex data needs. These aren't simple businesses. I'm seeing financial services companies managing billions in loans and payments, healthcare organizations coordinating care for millions of patients, retailers running hundreds of stores, and tech platforms serving tens of millions of users. What strikes me is how data-intensive their core operations are. Whether it's Komodo Health analyzing patient-level insights across healthcare systems, Genius Sports computing real-time data feeds for sports betting, or Avetta monitoring supplier risks across supply chains, these companies live and die by their ability to transform raw data into actionable intelligence.

These are predominantly mature, scaled enterprises. My analysis shows mostly companies with 200+ employees, many with 1,000+. I'm seeing post-Series C funding, private equity ownership, and multiple public companies. This isn't the scrappy startup phase. These are organizations with established product-market fit facing the operational complexity that comes with scale.