Northgrain Data joins the dbt Labs Partner Program

Northgrain Data has joined the dbt Labs Partner Program as a Registered Consulting & Services Partner.

This is less a change in direction than a formalisation of how we have been building data platforms from the beginning.

dbt sits at the centre of most analytics engineering work we deliver. We use it to turn fragmented transformation logic into maintainable data models that can be reviewed, tested, documented and deployed as code.

Joining the partner program allows us to deepen that focus while working more closely with the dbt ecosystem.

Why we build around dbt

Moving data into a warehouse is only the first part of building a useful data platform.

The harder problem begins when an organisation needs to turn raw source data into shared definitions of revenue, customers, inventory, retention or operational performance.

Without a structured transformation layer, that logic usually ends up distributed across:

• spreadsheets
• dashboard queries
• stored procedures
• standalone SQL scripts
• Python jobs
• individual analysts' knowledge

The result is rarely one dramatic failure. It is a growing collection of smaller problems: duplicated logic, inconsistent metrics, undocumented dependencies and changes that are difficult to review safely.

In our projects, dbt becomes the place where that business logic is structured and maintained.

Models are kept in version control. Changes go through review. Tests run before incorrect data reaches downstream reports. Documentation and lineage make it easier to understand how a source field eventually becomes a business metric.

How we use dbt in client projects

Our dbt work typically falls into several areas.

Building new analytics platforms

We design and implement the transformation layer as part of a complete data platform, including ingestion, warehouse architecture, orchestration, testing, monitoring and deployment.

Migrating existing reporting logic

We move transformations out of spreadsheets, BI tools, stored procedures and disconnected SQL scripts into a structured dbt project.

This creates a single place where business logic can be reviewed, tested and maintained.

Refactoring existing dbt projects

A dbt project can run successfully while still becoming increasingly difficult to work with.

We review project structure, model dependencies, materialisations, naming, testing coverage, documentation, CI/CD and warehouse performance, then implement the changes required to make the project easier to operate.

Productionising analytics workflows

We introduce the engineering practices required to move from a collection of models to a production system:

• automated testing
• CI/CD
• deployment environments
• source freshness checks
• documentation and ownership
• monitoring and alerting
• operational runbooks

What joining the partner program changes

The partner program gives Northgrain Data access to additional enablement, ecosystem resources and opportunities to work more closely with dbt Labs and other organisations in the dbt ecosystem.

For clients, our delivery model remains the same.

Projects are implemented in the client's environment. Code stays in the client's repositories. Testing, documentation, deployment and knowledge transfer are included in the scope rather than treated as optional additions.

The partnership strengthens the technical direction behind that delivery model.

Working with Northgrain Data

We currently support dbt and data platform initiatives through three engagement models.

Data Platform Audit

A structured review of an existing data platform or dbt project, followed by prioritised findings and a practical implementation plan.

Data Engineering Sprint

A fixed-scope implementation delivered over approximately three to six weeks. Typical projects include dbt migrations, platform builds, reporting automation and existing project remediation.

Embedded Data Engineering

Ongoing dbt, analytics engineering and data platform support for teams that require additional delivery capacity.

To discuss an existing dbt project or planned implementation, start with a Data Platform Audit.