dbt turned SQL transformations into real software: version-controlled, tested, documented, and deployed like code. But a dbt project grows fast. Without structure, you end up with hundreds of models nobody can navigate, tests that are missing or ignored, slow full rebuilds, and metrics defined three different ways.
Our dbt consulting services help you build dbt projects that stay clean as they grow. We set up dbt Core or dbt Cloud, design your project structure and modeling layers, add tests and documentation, build incremental models and snapshots, set up CI/CD, and define consistent metrics — so your analytics engineering is fast, reliable, and easy to maintain.
What are dbt consulting services? dbt consulting services help data teams implement and scale dbt (data build tool), the open-source framework for transforming data inside a warehouse using SQL and Jinja. Consultants design the project structure and modeling layers, write models, tests, snapshots, and macros, set up documentation and lineage, configure CI/CD and scheduling in dbt Core or dbt Cloud, and define shared metrics with the dbt Semantic Layer.
dbt Consulting at a Glance
Question | Answer |
|---|---|
What it is | Setting up, building, and scaling dbt projects for reliable data transformation |
Core deliverables | Project structure, staging, intermediate, and mart models, tests, documentation and lineage, CI/CD, scheduling, metric definitions |
Warehouses | Snowflake, BigQuery, Redshift, Databricks, PostgreSQL, Microsoft Fabric, and other dbt adapters |
Editions | dbt Core (open source) or dbt Cloud |
Typical timeline | A new dbt project foundation in a couple of weeks; refactors and migrations in phases |
Engagement models | dbt setup, migration to dbt, project audit and refactor, ongoing analytics engineering support |
Signs You Need dbt Help
Your transformations live in scattered SQL scripts or stored procedures. Nobody knows the run order or dependencies.
Your dbt project is hard to navigate. Models are named inconsistently and layers are mixed together.
Tests are missing or ignored. Bad data reaches dashboards before anyone notices.
Runs are slow and expensive. Large tables rebuild in full every time instead of incrementally.
Metrics are inconsistent. Revenue or active users are calculated differently in different models and tools.
No CI/CD. Changes go straight to production without automated checks.
Our dbt Services
dbt setup and onboarding
We set up dbt Core or dbt Cloud, connect your warehouse, configure environments and profiles, and establish Git workflows so your team can start building with best practices.
Project structure and modeling
We organize models into clear layers — sources, staging, intermediate, and marts — with naming conventions, folder structure, and materialization strategies that scale.
Migration to dbt
We convert stored procedures, SQL scripts, and legacy ETL transformations into dbt models, preserving logic and validating outputs against the original results.
Testing and data quality
We add generic and custom tests for uniqueness, nulls, relationships, accepted values, and business rules, plus source freshness checks, so problems are caught before they reach reports.
Incremental models and snapshots
We build incremental models for large tables and snapshots for slowly changing dimensions, cutting run times and warehouse costs.
Macros and packages
We write reusable Jinja macros and use proven packages such as dbt_utils to reduce duplicated SQL and keep logic consistent.
Documentation and lineage
We document models and columns and publish dbt docs, so analysts can see where every field comes from and how it's calculated.
CI/CD and orchestration
We set up pull-request checks that build and test only changed models, and schedule production runs in dbt Cloud, Airflow, or your orchestrator — integrated with your ETL / ELT pipelines.
Semantic layer and metrics
We define shared metrics with the dbt Semantic Layer (MetricFlow), so every BI tool and team uses the same definitions.
Project audits and refactoring
We review existing dbt projects for structure, performance, test coverage, and cost, then refactor the highest-impact areas.
dbt Modeling Layers
Layer | Purpose | Example |
|---|---|---|
Sources | Declare raw tables loaded into the warehouse | raw.stripe_payments |
Staging | One clean model per source table: renamed, typed, lightly cleaned | stg_stripe__payments |
Intermediate | Reusable joins and business logic | int_payments_joined_to_orders |
Marts | Business-ready tables for BI and applications | fct_orders, dim_customers |
dbt Core vs dbt Cloud
dbt Core | dbt Cloud | |
|---|---|---|
Cost | Free, open source | Paid, with a free developer tier |
Hosting | You run it on your own infrastructure | Managed by dbt Labs |
Scheduling and CI | Through your orchestrator and CI tool | Built in |
Best for | Teams comfortable managing infrastructure | Teams that want a managed environment and IDE |
We work with both and recommend based on your team, budget, and existing tools.
How It Works
Step 1: Assess
We review your warehouse, current transformations, data sources, and reporting needs.
Step 2: Design
We agree the project structure, layers, naming, testing standards, and deployment approach.
Step 3: Build
We build models layer by layer, starting with the sources that matter most to your reports.
Step 4: Test and validate
We add tests and compare outputs with your existing numbers.
Step 5: Deploy
We set up CI/CD and production schedules with alerts on failure.
Step 6: Train and hand over
We train your team on the project, conventions, and workflow, and document everything.
What You Get
A well-structured dbt project with clear layers and conventions
Tested, documented models with lineage
Faster, cheaper runs through incremental models
CI/CD and scheduled production runs
Consistent metric definitions
Training for analysts and analytics engineers
Who This Is For
Teams new to dbt | Teams with messy dbt projects | Companies on stored procedures | BI-heavy organizations |
|---|---|---|---|
Want to start with the right structure | Need an audit, refactor, and better tests | Need transformations moved into version-controlled dbt | Need one set of metric definitions for every dashboard |
Why Work With Codersarts
We treat analytics code like production software. Our team works across warehouses such as Snowflake and Databricks, pipelines, and BI, so your dbt project fits cleanly between ingestion and reporting. We set standards your team can follow long after handover.
Frequently Asked Questions
What is dbt used for?
dbt is used to transform raw data inside a data warehouse into clean, tested, business-ready tables using SQL. It handles dependencies, testing, documentation, and deployment, so transformations are managed like software.
Is dbt an ETL tool?
dbt handles the "T" — transformation — in ELT. It doesn't extract or load data; tools like Fivetran, Airbyte, or custom pipelines load data into the warehouse, and dbt transforms it there.
Should we use dbt Core or dbt Cloud?
dbt Core is free and flexible but needs your own scheduling and CI. dbt Cloud adds a managed IDE, scheduling, CI, and the Semantic Layer. The best choice depends on your team's skills and budget.
Can you migrate our stored procedures to dbt?
Yes. We convert procedures and scripts into modular dbt models, add tests, and validate that results match the original logic.
How much do dbt consulting services cost?
A new project foundation or audit is a fixed-scope engagement. Migrations and large refactors depend on the number of models and procedures, and are quoted after an assessment.
How do you make dbt runs faster?
Through incremental models, better materialization choices, avoiding unnecessary rebuilds, running only changed models in CI, and optimizing the heaviest SQL.
Does dbt work with Power BI and Tableau?
Yes. dbt builds the mart tables those tools read from, and the Semantic Layer can supply shared metric definitions to supported BI tools. See business intelligence.
Do you offer ongoing dbt support?
Yes. Monthly analytics engineering support covers new models, fixes, test coverage, and performance tuning.
Related Data Services
Data Warehouse Consulting — the warehouse your dbt project runs on
Data Engineering — ingestion and pipelines that feed dbt
Data Strategy Consulting — standards and roadmap for your data stack
SQL Queries — SQL writing and tuning
Ready for Cleaner, Tested Transformations?
Tell us about your warehouse and how transformations work today. We'll show you how a dbt setup would look and what it would take.
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