DataWarehouseArchitect

Designing a modern data warehouse architecture with dbt involves selecting a warehouse like Snowflake or BigQuery, structuring dbt models into staging, intermediate, and marts layers, and integrating data quality checks to build a trusted analytics foundation.

Structuring dbt transformation layers requires defining staging, intermediate, and marts models to organize data flows, enabling repeatable transformations and supporting self-serve analytics across the enterprise data stack.

Choosing between Snowflake, BigQuery, and Redshift requires evaluating data warehouse sizing, cost considerations, and specific platform architecture needs to determine the best fit for your enterprise analytics platform.

Integrating data quality and cataloging into an ELT pipeline involves embedding data quality checks within ingestion workflows and implementing data cataloging and lineage tracking to enable governed, self-serve analytics.

This data architecture approach supports designing ingestion pipelines for both batch and streaming data, accommodating EL, CDC, and data quality integration to unify data engineering and governance workflows.

Setting up metrics governance and data lineage requires implementing data cataloging and governance frameworks across the transformation layers to ensure trusted dashboards and enable self-serve analytics.