Partner

dbt

The transformation layer in every data platform engagement we deliver — bringing software engineering discipline to analytics with tested, version-controlled data pipelines.

Joint value proposition

Analytics engineering with software discipline — tested, documented, and version-controlled data transformations at enterprise scale.

dbt provides the transformation framework. Proxima implements it as the standard layer between raw data ingestion and consumption — whether the platform is Databricks, Snowflake, or Azure Synapse. Every model tested, every lineage tracked, every change reviewed through CI/CD.

What we deliver together

  • dbt Cloud and dbt Core implementation — project structure, environment strategy, and CI/CD integration with Azure DevOps or GitHub Actions
  • Medallion architecture with dbt — bronze, silver, gold layer transformations with automated testing and data quality contracts
  • Data lineage and documentation — auto-generated documentation and column-level lineage for compliance and governance requirements
  • Platform integration — dbt on Databricks, Snowflake, Azure Synapse, and Microsoft Fabric with optimized materialization strategies

Industry focus

Where we deliver dbt solutions

Financial Services

Auditable transformation pipelines for regulatory reporting, risk calculations, and BCBS 239 data lineage requirements.

Healthcare

HIPAA-compliant data transformations with automated PII detection, masking logic, and full audit trail.

Energy

Operational data transformations for grid analytics, meter data management, and trading platform reporting.

Reference delivery pattern

Enterprise dbt implementation for a financial services data platform

Implemented dbt Cloud as the transformation layer for a Databricks lakehouse serving regulatory reporting and risk analytics. Built 200+ models across medallion layers with automated testing, freshness checks, and data contracts enforced at the gold layer.

Integrated with Azure DevOps for CI/CD — every model change goes through pull request review, automated testing in a staging environment, and deployment approval gates. Column-level lineage satisfies BCBS 239 requirements without manual documentation.

Data pipeline failures reduced by 80%. Regulatory report generation time cut from 3 days to 4 hours. Full lineage from source to report for every metric.

Ready to bring engineering discipline to your data?

Talk to a data architect about implementing dbt in your analytics stack.

Schedule a Discovery Call