What companies are using dbt technologies for their data analytics?
Data build tool or dbt is an open-source tool developed by dbt Labs that enables data engineers and analysts to transform raw data directly within their data warehouse using SQL. It is widely used by companies that need analytics to be reliable, testable, and repeatable. It brings version control, testing, documentation, and modular development into the analytics workflow. That matters once data stops being a reporting layer and starts affecting finance, operations, product decisions, and AI workflows.
This is why the better question is not simply which companies use dbt. The better question is what kind of company needs it. Usually, it is a company with multiple teams, growing data volume, shared metrics, and low tolerance for broken logic or inconsistent reporting. dbt is built for that stage.
Its adoption is no longer limited to early-stage data teams. dbt says its community includes more than 100,000 members, and the company has also said that 50,000+ teams use dbt weekly. That makes it part of the mainstream analytics stack for modern data teams.
| Metric | Number | Why It Matters |
|---|---|---|
| dbt community size | 100,000+ members | Shows ecosystem maturity |
| Teams using dbt weekly | 50,000+ | Shows repeat operational use, not niche adoption |
| Finance testing example | 3,000 tests/day | Signals reliability and control |
| Large-scale transaction example | 100–125B transactions/day | Shows suitability for serious enterprise scale |
| Migration example | 1,200 models in 3 months | Shows implementation speed at scale |
| Reliability example | 99.9% uptime | Speaks to production readiness |
In this article, we’ll look at companies using dbt, what they use it for, and what that says about the state of enterprise analytics.
The Infrastructure Shift: From Dashboards to Data Products
Before diving into specific industries, it’s important to understand the fundamental shift in how these companies view data. In the old world, data was a byproduct, something you cleaned up once a month to build a slide deck. In the dbt world, data is a living, breathing piece of software that requires an "assembly line" to stay functional.
This shift happens when a company realizes that their data warehouse isn't just a graveyard for logs; it’s the engine room for the entire business. When you reach this stage, you stop asking "How do I write this query?" and start asking:
Who owns this logic? (If the Head of Marketing and the Head of Sales define "Revenue" differently, the system is broken.)
How do we know it’s broken? (Manual spot-checks aren't enough when you're processing millions of rows per hour.)
Can we recreate this? (If your lead analyst quits, does the knowledge of how your data works walk out the door with them?)
Companies across every sector use dbt to solve these "Software Engineering" problems within their data teams. They are moving away from fragile, "black-box" ETL tools and toward a transparent, code-based workflow.
1. Finance and Fintech
Finance teams adopt dbt when reporting has to hold up under pressure. The requirements are familiar: consistent metrics, clear lineage, faster closes, and less manual reconciliation. dbt fits that environment because it adds testing, documentation, and repeatable transformation logic to the analytics layer.
The company examples are telling. Rocket Money says it runs 3,000 tests daily and passed its first SOX audit with zero deficiencies after modernising its financial reporting stack with dbt. Bilt Rewards says it cut analytics costs by 80% by moving embedded analytics logic into the dbt Semantic Layer. Nasdaq operates 100 to 125 billion transactions per day, onboarded 55 business users into modelling and reporting, and reduced one key model runtime from about one hour to 10 minutes. Pepperstone reports a 30% increase in speed to delivery after moving to dbt Cloud.
The pattern is straightforward. In finance and fintech, dbt is used to make data work less like a loose reporting layer and more like controlled infrastructure. That is usually the point where adoption starts making sense.
2. Travel, Retail, and Consumer Brands
dbt is also being used by companies with high transaction volume, fragmented systems, and fast-moving reporting needs. In these environments, the problem is usually not lack of data. It is too many sources, too much business logic, and too little standardisation. dbt helps centralise transformation logic and make it easier to maintain.
JetBlue migrated 26 data sources and 1,200 models in 3 months and reported 99.9% uptime with no increase in total cost of ownership. For a regulated business with operational and customer data moving at speed, that is a meaningful outcome.
In retail, J.Crew used dbt during a broader modernisation effort and reported 50% faster onboarding for its data team, with zero production pipeline issues during a critical implementation window. That matters in retail because migration mistakes tend to show up at the worst possible time.
Consumer brands are using dbt for the same reason. McDonald’s Nordics used it to centralise data across 4 markets, track €1.3 billion in sales, and improve delivery time for historical data by 5x. It also gave the team a cleaner structure for traceability and downstream reporting.
In travel, retail, and consumer businesses, dbt is being used to standardise analytics across messy operational environments. That is usually the point where the stack has to grow up.
3. Media, SaaS, and Digital Platforms
dbt is also being used by companies that need more people to work on data without turning the workflow into a mess. In these environments, the value is usually not just faster SQL. It is better collaboration, clearer lineage, and a more stable path from raw data to reporting, machine learning, and product use cases.
Condé Nast is a good example. The company says its platform built with dbt helped increase self-service by 30%, supported 85 dbt models, and saved 16 hours per data integration sprint project. It also used the setup to give teams across regions access to the same analytics-ready and business-ready datasets.
SpotOn used dbt Cloud as part of a broader modern data stack shift. The company says time to actionable insights improved by 6x, annual savings reached $110,500, and contributor count to ETL code grew from 2 to 17 people. It also used dbt to support internal analytics and product-facing reporting.
Fullscript shows another common pattern. During a migration, the company says it used dbt Cloud to build a report in days instead of months. Then, during an acquisition, its data sources effectively doubled overnight. Fullscript says it was able to extend existing models and still complete the migration within the planned nine-month period.
In media, SaaS, and digital businesses, dbt is often used to make analytics easier to scale across teams without losing control of the logic underneath.
4. Healthcare and Mission-Critical Platforms
dbt is also being used in healthcare-adjacent environments where data quality affects product experience, internal decisions, and downstream operations. In these settings, reliability matters more because broken models do not stay confined to a low-code dashboard.
Vivian Health saw a 3x increase in data pipeline contributors, built 100% of its data models in dbt, and uses dbt across analytics, internal dashboards, and machine learning training data. It also describes dbt as a critical part of its infrastructure.
That matters because it shows where dbt fits in a more mature stack. It is not just being used to clean tables for reporting. It is being used to support operational analytics, business applications, and product-facing systems in the same environment.
The pattern holds here too. Companies use dbt when data logic needs to be shared, tested, and trusted across teams. In healthcare and other mission-critical environments, that threshold arrives early.
5. What These Companies Have in Common
The companies using dbt are different. The reasons are not. They usually reach the same point in the lifecycle: more data, more teams, more reporting, and less room for ambiguity. dbt becomes useful when analytics logic has to be shared, tested, documented, and maintained like production code. That is how dbt itself frames the platform, and the case studies reflect it.
A few things show up repeatedly.
First, these companies are trying to reduce dependency on a small central data team. JetBlue used dbt as part of a shift away from a bottlenecked central model, while Condé Nast used it to increase self-service and give more teams access to analytics-ready data.
Second, they need stronger trust in the numbers. Rocket Money used dbt to support a quote-to-cash system with 3,000 daily tests and passed its first SOX audit with zero deficiencies. Vivian Health describes dbt as critical infrastructure and uses it across internal dashboards and machine learning training data.
Third, they need analytics to support operations, not just dashboards. That is clear in JetBlue’s migration of operational and customer data, and in Condé Nast’s use of dbt-ready data for reporting, machine learning, and content personalization.
So the path is simple. Companies use dbt when data stops being an isolated reporting function and starts becoming shared business infrastructure. That is where dbt tends to earn its place.
6. How to Manage dbt With an Application Layer
dbt handles transformation, testing, documentation, and deployment discipline in the data layer. Low-code platforms like ToolJet fit further up the stack. Its app builder is designed for internal tools such as dashboards, approval workflows, tracking systems, and scheduling apps. Its query layer connects app UI to SQL, NoSQL, vector databases, APIs, spreadsheets, and cloud services.
That makes the fit fairly direct. Teams can use dbt to define and govern data models, then use ToolJet as the application layer where business teams actually work with that data. ToolJet also directly supports warehouse connections such as Snowflake, BigQuery, CouchBase, and Databricks. That makes it a reasonable layer for internal dashboards, review flows, finance tools, and operational consoles built on top of governed data.
7. Conclusion
The companies using dbt span finance, retail, travel, healthcare, media, and SaaS. The pattern is consistent. They use dbt when analytics needs stronger governance, clearer lineage, tested transformation logic, and a shared development workflow across teams. That is also how dbt describes the platform: software engineering practices applied to analytics so data work becomes more reliable and scalable.
The company examples make that concrete. Rocket Money used dbt in a SOX-sensitive reporting environment, JetBlue used it across a large migration of sources and models, and Vivian Health uses dbt across analytics, internal dashboards, and machine learning training data.
So the answer to the original question is simple. Companies use dbt when data becomes core infrastructure rather than a support function. At that point, the value is less about writing SQL faster and more about making analytics dependable enough to run the business.
