dbt consulting
dbt consulting for healthcare analytics teams.
Turn brittle SQL, unclear metrics, and dashboard mistrust into tested models, documented lineage, useful reviews, and data products that health-tech business teams can understand.
Make the number explainable.
Healthcare analytics teams carry a high cost when a metric is hard to trace, a definition changes without notice, or the person who knows the logic is unavailable. dbt creates a practical workflow for turning transformation logic into a shared, reviewable data product.
- Tested modelsMake critical assumptions visible through model tests and repeatable checks.
- Documentation and lineageShow where a metric comes from, what it means, and which decisions depend on it.
- Business translationGive operators and executives language they can use when they question a number or request a new one.
- Team workflowUse reviews, ownership, conventions, and handoff so the system does not depend on one person’s memory.
A cleaner path from SQL to trust.
Start where the decision pressure is highest. The work can focus on a critical domain, a fragile reporting flow, or the conventions a team needs to scale.
- Project assessmentReview model structure, naming, tests, documentation, dependencies, and the workflow around delivery.
- Critical model buildRefactor or create the models that support the most important business questions.
- Metric and semantic clarityAlign definitions, grain, ownership, and expected use before dashboards multiply disagreement.
- Quality and lineageAdd the checks and documentation that make failure easier to detect and explain.
- Review and handoffLeave conventions, pull-request patterns, runbooks, and a team that can continue the work.
- AI-ready foundationMake the data products, context, and ownership clearer before model-assisted work depends on them.
Claims lag & capital float calculator.
Clearinghouse batching and uncalibrated claims models lock millions in unadjudicated float while distorting monthly Medical Loss Ratios. Estimate your exposure in 15 seconds (100% client-side, zero data sent).
Clarity beats complexity.
dbt is useful because it puts transformation logic where a data team can test, review, document, and improve it.
- Define the grainMake the unit of analysis and intended use explicit.
- Model the logicUse modular SQL that reflects the business concepts the team needs.
- Test the contractCatch bad assumptions before they become executive questions.
- Document the pathLeave context, lineage, ownership, and a workflow for change.
dbt in business terms.
Why use dbt in a healthcare data environment?
dbt provides a shared workflow for versioned SQL models, tests, documentation, lineage, review, and ownership. That makes critical metrics easier to inspect and easier for business teams to trust.
Can you work with our existing warehouse?
Yes. The starting point is the current data environment and the decisions it needs to support. The engagement can improve the dbt project and workflow without assuming a platform migration.
Is this only for a large analytics team?
No. A small team can benefit from clear conventions, tests, documentation, and handoff. The scope should match the critical data products and the people who maintain them.
What healthcare-specific dbt tests do you enforce?
We enforce surrogate key grain uniqueness, bitemporal enrollment consistency (detecting effective, enrollment, and updated date desync), CMS NPI provider registry lookups, ICD-10 and HCPCS/CPT code validity, default midnight (00:00:00) timestamp audits, and referential integrity between claims and member spans. We also maintain the open-source dbt test package refhealth-dbt-healthcare-checks and the cloneable reference repository refhealth-starter-dbt-project.
Need your analytics to survive scrutiny?
Share the metric, reporting flow, or model layer that is creating friction. The next step can be a paid diagnostic, dbt build sprint, data landscape assessment, or fractional leadership engagement.