Tested, documented SQL transformations your analysts can trust.
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§ Signs
Our dbt development services turn raw data into tested, documented models in your warehouse or lakehouse. We implement dbt Core and dbt Cloud, write SQL-based transformations with sources, seeds, snapshots and incremental models, and model data with star schemas, dimensional models or Data Vault. Every project ships with data tests, documentation and lineage, and runs through Git integration and CI/CD so changes are reviewed and deployed safely. We build dbt on Snowflake, Databricks and Microsoft Fabric, and help analytics teams adopt analytics engineering practices that last.
dbt helps most where business logic is scattered or untested. It can also become its own mess without structure.
Business rules live in stored procedures, BI tools and spreadsheets, and the same metric is defined three ways.
Problems surface in a board pack instead of a failed test.
Hundreds of models, no layering, full refreshes everywhere and runs that take longer every month.
No CI, no review and no way to see which downstream models a change will affect.
Every small change to a report needs an engineering ticket and a sprint.
§ What we deliver
Choose an area to see the services in it.
dbt Coredbt Cloud
Microsoft Fabric§ Proof
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§ Approach
We review existing SQL, stored procedures or dbt projects, sources and reporting needs.
You receive dbt project assessment and modelling plan
dbt Core or dbt Cloud, environments, Git workflow and project conventions are put in place.
You receive Configured dbt project and Git workflow
Sources, staging, intermediate and mart models are built, using star schema or Data Vault patterns.
You receive Tested dbt models for agreed subject areas
Data tests, documentation and lineage are added for every model.
You receive Published dbt docs and test coverage
CI/CD runs builds and tests on every pull request and deploys on merge.
You receive CI/CD pipeline for the dbt project
We leave a style guide, a contribution guide and a walkthrough so analysts and engineers can add models through pull requests.
You receive Style and contribution guides, onboarding walkthrough
§ Tech stack
dbt Coredbt CloudMicrosoft Fabric
Azure DevOps§ Engagement models
A defined scope, timeline and deliverables, from design through go-live.
An agreed price for a clearly scoped implementation, such as a migration or a new warehouse.
Our engineers work under your contract, or as a subcontractor to consulting firms and system integrators.
Data engineers who join your team and work in your tools and processes.
Consultants and developers working remotely, with overlap in your working hours.
Architecture reviews, platform choices and design guidance for your own team.
Monitoring, incident handling and ongoing fixes for pipelines and databases in production.
A short, fixed-scope engagement to test an approach on your data or plan a migration.
§ FAQs
We implement both. The choice depends on how you want to schedule runs, manage environments and give analysts access; we set up whichever fits your team and platform.
Snowflake, Databricks and Microsoft Fabric.
Yes. We rewrite SQL-based transformations as dbt models with sources, tests and documentation, and use incremental models and materialisations to keep run times down.
Both are supported. Star schema and dimensional modelling suit reporting marts; Data Vault suits integrating many changing sources with full history. Many projects use Data Vault underneath and star schemas on top.
With data testing and data quality frameworks in dbt: tests run in CI on every change and on every scheduled run, and failures alert the team.
Yes. We audit the project, restructure layers, move heavy models to incremental builds, remove unused models and fix slow runs, without breaking the dashboards that depend on them.
With generic tests for keys and relationships, singular tests for business rules, unit tests for complex logic and source freshness checks, all run in CI before a change is merged.
dbt Cloud jobs where you use dbt Cloud. Otherwise Apache Airflow, Dagster, Azure Data Factory or Databricks Workflows, whichever you already run.
It depends on the number and complexity of procedures. We inventory them, convert in waves by business domain and reconcile each wave against the old output, so value arrives before the whole estate is done.
§ In detail
Staging, intermediate and mart layers, naming rules and the packages that suit your warehouse.
Stored procedures and legacy SQL rebuilt as dbt models, with incremental strategies and snapshots for history.
Generic, singular and unit tests plus source freshness checks, run on every pull request.
A dbt docs site with column descriptions and exposures that link models to the dashboards they feed.
Slim CI that builds only changed models and their dependants, and production jobs in dbt Cloud, Airflow or your existing orchestrator.
Metrics defined once in the dbt Semantic Layer where you use dbt Cloud, so every tool reports the same number.
We coach analysts to write models and tests through pull requests, with engineers reviewing.
Converted models run next to the legacy logic until the outputs match and owners sign off.
The project lives in your Git and runs on your Snowflake, Databricks, Fabric or PostgreSQL.
Every change goes through review and CI, including ours.
Product, billing and CRM data turned into recurring revenue, retention and churn metrics defined once.
Orders, returns and marketing spend modelled into margin and attribution marts.
Snapshots that keep history, and tested, version-controlled transformations an auditor can follow.
Masking macros and access policies that keep identifiable fields out of analyst-facing marts.
§ Contact
Starting with dbt or untangling an existing project? Tell us about it.
