Loose SQL becomes a version-controlled, tested, traceable model.
I organize your transformation layer with dbt and SQL: staging, marts, tests and lineage. Instead of loose queries no one understands, you get a version-controlled model where every number has a traceable origin.
A human reply · a diagnostic before any build · mutual NDA
Recognize any of these symptoms?
- dbt structure: staging, intermediate and marts
- Automated data tests
- Generated lineage and documentation
- Version control and review of transformations
- 1
Survey
I map the transformations that hold up your numbers today.
- 2
Structuring
I reorganize into dbt layers, with clear names and responsibilities.
- 3
Tests
I add tests that catch the error before it reaches consumption.
- 4
Documentation
Lineage and docs so the team understands where each number comes from.
Common questions about analytics engineering.
I already use dbt — can it be improved?
Yes. I review the structure, tests and lineage of an existing dbt project — it's real experience I bring from production data warehouses.
Is dbt right for a small company?
It's right when there's enough transformation to justify version control and tests. In the diagnostic I tell you whether it's worth it or whether well-organized SQL already does the job.
Which database does it work with?
dbt runs on PostgreSQL, BigQuery, Snowflake, SQL Server and others. I adapt to what you already use.
Want me to look at your case?
A 30-minute call, no commitment. I'll tell you where the risks are and what to fix first.