dbt consulting
dbt Health Check
A focused review of your dbt project that surfaces what is slowing your team down, what best practices are missing, and where the biggest improvements are hiding.
Most dbt Projects Drift From Best Practices Over Time
Your team adopted dbt to bring structure to data transformation. It worked. But as models multiplied, new people joined, and business requirements shifted, the project started showing strain: runs take longer, debugging eats hours, documentation is thin, and nobody is sure the tests actually catch the right problems.
This is normal. dbt projects that were well-designed for ten models behave differently at two hundred. Teams that learned dbt on the job often miss patterns that only become obvious once the project reaches a certain scale.
Common symptoms we see:
- Slow model runs that delay dashboards and reporting downstream
- Fragile pipelines where one upstream change breaks models nobody expected
- Sparse or missing tests that let bad data reach production undetected
- Inconsistent patterns across models written by different people at different times
- Thin documentation that makes onboarding new analysts painful
- Underused features like incremental models, contracts, model versions, or the semantic layer
A dbt health check gives your team an outside perspective on where the project stands today and a concrete plan for what to fix first.
A Structured Review Across Code, Process, and Team
We do not just scan your YAML files and send a checklist. A health check examines how your dbt project actually works in practice: the code patterns, the development workflow, the deployment pipeline, and how your team interacts with the project day to day.
Project Structure and Data Modeling
Are staging, intermediate, and mart layers clean and consistent? Are sources well-defined? Is there unnecessary duplication or circular logic in your DAG?
Testing and Data Quality
Do your tests catch real problems or just check boxes? Are you using generic tests, singular tests, and source freshness checks where they matter? Are contract and unit tests in place for critical models?
Performance and Cost
Which models are expensive to run and why? Are materializations correct? Could incremental models, clustering, or query optimization reduce run times and warehouse spend?
CI/CD and Development Workflow
Is your team using version control, pull request workflows, and CI checks effectively? Are environments configured properly for development, staging, and production?
Documentation and Discoverability
Can a new team member understand what a model does without reading the SQL? Are descriptions, column-level docs, and metadata current and useful?
Orchestration and Monitoring
How are jobs scheduled and monitored? Are failures caught and surfaced quickly? Is your orchestration strategy aligned with how downstream consumers use the data?
A Prioritized Roadmap, Not Just a List of Findings
The point of a health check is not to produce a document that sits in a shared drive. You get a prioritized remediation roadmap that tells your team exactly what to fix, in what order, and why it matters.
Health Check Deliverables
Assessment Report covering every review area with specific findings, severity ratings, and examples pulled directly from your project.
Prioritized Remediation Roadmap that separates critical fixes from nice-to-haves, estimates effort for each recommendation, and sequences work so your team sees results quickly.
Architecture Recommendations for project structure, materialization strategy, testing approach, and deployment workflow improvements tailored to your data platform and team size.
Skills and Process Gap Analysis identifying where your team could benefit from new patterns, better tooling, or targeted training on dbt features they are not using yet.
Executive Summary translating technical findings into business impact: pipeline reliability, data freshness, warehouse cost, and team velocity.
Optional: Hands-On Remediation
Some teams want the roadmap. Others want help executing it. We can stay engaged after the assessment to implement the highest-priority fixes, pair with your engineers to establish new patterns, and ensure the improvements stick.
A Short, Focused Engagement Designed for Minimal Disruption
A dbt health check is not a six-month consulting project. It is a focused engagement, typically two to four weeks, that gives your team clarity without pulling them away from their work.
Week 1: Discovery
We talk to your data engineers, analytics engineers, and stakeholders to understand how the project is used, what frustrates the team, and what business outcomes depend on the pipeline. We get read access to your dbt project repository and environment.
Weeks 2-3: Deep Review
We work through the project systematically: structure, code patterns, testing, documentation, performance, CI/CD, orchestration, and governance. We review the DAG, run analysis queries, benchmark model performance, and compare patterns against current dbt best practices.
Week 3-4: Roadmap and Readout
We present our findings and remediation roadmap to both the technical team and leadership. Every recommendation includes context, priority, estimated effort, and expected impact. We leave time for questions and help your team plan next steps.
Ready to See Where Your dbt Project Stands?
If your dbt project is in production but you suspect it could be faster, more reliable, or easier to maintain, a health check will confirm what is working and show you exactly where to improve.
Schedule a dbt Health Check Conversation to discuss your project, timeline, and goals.
Next step
Wondering what a health check would find in your dbt project?
Tell us about your dbt setup, what is working, and what feels off. We will help you figure out whether a health check is the right next step.