dbt is how we turn business logic from tribal knowledge into managed code: reviewed, tested, documented, and deployed deliberately. It’s the backbone of nearly every analytics platform we build.
The problems this solves
Competing definitions
A core metric is defined in three or four places, each slightly different, and the differences stay invisible until someone compares outputs.
Fragile reports
Reports break when someone edits a query nobody documented, and the fix depends on finding who wrote it.
Key-person risk
One person understands how the transformations work. That’s the disaster recovery plan.
Slow to change
Every reporting change takes weeks, because nobody fully trusts what a deploy will do.
From scattered logic to governed code
The shift dbt makes is simple to state: business logic stops living in spreadsheets, BI tools, and someone’s memory, and starts living in code that can be reviewed, tested, and trusted.
Comparison diagram: before dbt, business logic is scattered across spreadsheet formulas, BI-tool calculated fields, undocumented SQL scripts, and one person’s memory. With dbt, logic lives in one governed repository where changes are proposed, reviewed, and merged, tests run at build time, documentation is generated as a by-product, and deployments are deliberate with clean rollbacks. Business outcomes: more trust in numbers, faster development cycles, less key-person dependency, and greater flexibility when the business changes.
What we do with dbt
From a first project structure to a mature analytics engineering practice your team runs themselves.
- dbt project architecture, standards, and repo structure
- Migration of legacy SQL and stored procedures into dbt models
- Automated testing that catches breakages at build time
- CI/CD for analytics code: reviewed, merged, deployed deliberately
- Documentation and lineage generated as a by-product of the work
- Incremental models and performance tuning as volumes grow
- Semantic definitions aligned between dbt and the warehouse
- Team enablement: code review practice your analysts will keep
Outcomes
reduction in manual reporting effort for a manufacturing client, after we centralized their reporting on Snowflake with structured dbt models
Less time preparing data, more time analyzing it. That’s the trade every reporting team is trying to make, and version-controlled models are how it sticks.
Frequently asked questions
Is dbt worth it for a small team?
Often it matters most for small teams, because they carry the most key-person risk. When one analyst holds the transformation logic in their head, dbt is how it becomes legible to everyone else. You can start with a single domain and a handful of models.
dbt Cloud or dbt Core?
It depends on your team, budget, and how much orchestration you already run. We implement both and will lay out the tradeoffs plainly: Cloud buys convenience and a managed scheduler; Core buys flexibility and a smaller bill, with more to operate yourself.
Can you migrate our existing SQL into dbt?
Yes, and it’s one of our most common engagements. We translate legacy scripts and stored procedures into modeled, tested dbt code incrementally, validating outputs against the originals as we go. Migration is also the cheapest moment to add tests you should have had all along.
How does dbt connect to our AI plans?
Directly. AI answers are only as good as the modeled logic underneath them. Version-controlled, tested dbt models are what make a semantic layer trustworthy, and a trustworthy semantic layer is what makes AI answers defensible.
Where this connects
Logic your whole team can read
If your reporting depends on knowledge in one person’s head, that’s the risk to fix first. We’ll show you what it takes.
Get In Touch