Project and model structure
Establish naming, dependencies, source definitions and ownership around the analytical domains the team maintains.
dbt
dbt models your team can review and reuse.
Move analytical transformation logic into a maintainable dbt project with explicit model grain, dependencies, tests and documentation. Connect releases to the way your analytics team works.
Platform architecture
A transformation graph with business meaning
dbt sits between available source data and the models consumed by analytics. The project should expose dependencies and assumptions, making changes reviewable before they affect downstream reports.
Sources and staging
Declare source relationships and normalize inputs while retaining the meaning of source fields and identifiers.
Boundary: Source freshness, keys and ingestion ownership
Intermediate models
Compose reusable transformation steps with explicit joins, grain and dependency references.
Boundary: Model contracts and transformation dependencies
Business marts
Publish datasets around agreed business entities and measures for analytical consumers.
Boundary: Metric definitions and historical behavior
Tests and release
Run selected checks, review changes and document model dependencies through the delivery workflow.
Boundary: Test evidence and production run ownership
Across the system
- Version control
- Model ownership
- Data tests
- Dependency review
Before choosing the stack
Decisions worth making early.
- Where should transformation logic live?
- Place reusable business logic in governed models where appropriate. Presentation calculations can remain in BI tools, but shared definitions need a clear authoritative location.
- Which execution setup fits the team?
- Evaluate the available dbt tooling, supported adapters, orchestration and hosting responsibilities. The choice should fit team ownership and platform compatibility rather than a feature checklist alone.
- When should a model be incremental?
- Use incremental logic when it provides a measured benefit and correctly handles late updates, deletes and backfills. A faster run is not useful if it silently produces incomplete history.
Vendor documentation informs platform selection; it does not imply a vendor partnership or certification.
Before you commit
Is this the right engagement?
Versioned analytical transformations, model dependencies and data tests within a supported data platform.
- What we need from you
- Warehouse access, source schemas, existing SQL and reports, metric owners, orchestration setup and representative historical changes.
- How you accept the work
- Compare model outputs with agreed business definitions, execute meaningful data tests and rehearse deployment and backfill behavior for the selected models.
- Scope & alternatives
- dbt transformation work starts from accessible source data. New ingestion infrastructure and source-system fixes require a separate data engineering scope.
Capabilities
What we can implement with dbt
Select the relevant work after reviewing your existing environment. The proposal records deliverables, dependencies and ownership.
Transformation migration
Move selected SQL logic into reusable models while reconciling joins, historical treatment and downstream reporting behavior.
Testing and documentation
Add checks for meaningful data assumptions and document business context, not only the technical column names.
Development workflow
Connect model review, execution and releases to existing repositories and orchestration, including responsibility for failed runs.
Frequently asked questions
Questions about dbt
01Does dbt replace data ingestion?
Does dbt replace data ingestion?
This engagement focuses on transforming data already available in the analytical platform. Source connectivity, extraction and raw loading need an ingestion path, whether existing or scoped separately.
02Can dbt fix inconsistent business metrics?
Can dbt fix inconsistent business metrics?
It can provide a shared implementation, but stakeholders still need to agree definitions. We record metric ownership and reconcile competing calculations before publishing a reusable model.
03Should every model have the same tests?
Should every model have the same tests?
No. Choose tests from the model’s assumptions and business impact. Keys, relationships, accepted values and custom business rules require different checks and responses when they fail.
04Can you migrate an existing dbt project?
Can you migrate an existing dbt project?
Yes. Inspect adapter compatibility, dependencies, orchestration and custom logic, then validate a representative model set and downstream consumers before switching production runs.
05How do you handle changes to historical data?
How do you handle changes to historical data?
Define whether the source corrects records, tracks versions or supplies late updates. The model design and backfill procedure must preserve the agreed business history and be tested against those cases.
Start a conversation
Make the next dbt decision with a clear scope.
Bring the current architecture, the constraint and the outcome you need. We will identify the next useful increment and the evidence required to accept it.