Fivetran + dbt Transformations Consulting

Fivetran and dbt Labs completed their merger on June 1, 2026. For anyone running Fivetran, that settles a question that used to take a design meeting: the transformation layer on top of Fivetran-loaded data is dbt, with SQLMesh (from Fivetran's 2025 acquisition of Tobiko Data) as the alternative for teams that want it. Fivetran syncs stay ELT, landing raw data in the destination; dbt turns it into the models your analysts and applications actually use.

Most of the transformation work we see still lives in the wrong place: scheduled SQL in the warehouse, views nobody owns, or notebooks that run when someone remembers. This service moves it somewhere maintainable.

What we do

Choose the right way to run dbt. There are three reasonable options and they suit different teams:

• Fivetran Transformations with Quickstart data models. Pre-built dbt packages for common sources (Salesforce, HubSpot, ad platforms, and many more) scheduled by Fivetran to run when the sync completes. Fastest path to a usable staging layer.
• Fivetran Transformations with your own dbt project. Your repository, your models, still triggered off sync completion so that models never run against half-loaded data.
• dbt Cloud or a self-hosted dbt Core scheduler. For teams with an existing dbt practice, multi-source DAGs, or orchestration in Airflow or Dagster.

We help you pick, and we will say plainly when the simplest option is the right one.

Migrate legacy transformations. We inventory your scheduled SQL, stored procedures, and BI-tool prep queries, map them to a layered dbt project (staging, intermediate, marts), and migrate them with tests proving the outputs match before the old jobs are switched off.

SQLMesh where it fits. For teams that need virtual environments, column-level lineage, or cheaper incremental backfills, we evaluate and implement SQLMesh as the transformation engine instead of, or alongside, dbt.

Orchestration. Transformations should run when data arrives, not on a guessed cron. We wire models to Fivetran sync completion and, where you have an orchestrator, integrate with it rather than fighting it.

Testing and CI. Every project we deliver has schema tests, source freshness checks, and a CI job that builds changed models against a slim copy of production before merge. A transformation layer without tests is a future incident.

Governance. Documentation generated from the project, ownership per mart, and a review process so that model changes are treated like code changes.

Typical deliverables

• A written assessment of your current transformations and a target dbt (or SQLMesh) architecture
• A working project in your repository with staging models for every Fivetran source in scope
• Migrated marts with parity tests against the legacy outputs
• CI configuration, documentation site, and a runbook for your team

Who this is for

Teams that have Fivetran syncing reliably and now need the data to be trustworthy and modeled, teams inheriting a transformation layer nobody understands, and teams planning a combined Fivetran + dbt account who want it set up right the first time. It pairs naturally with our data warehousing consulting and cost optimization services.

Contact us to talk through your transformation layer, or read our tutorial on what the dbt merger changes for your pipelines.