DataWarehouseArchitect

What problem does it solve?

Design and implement modern data platform architectures that consolidate data warehouses, transformation layers, ingestion pipelines, data catalogs, and governance to empower analytics at scale.

Core Features & Use Cases

• Data warehouse selection and sizing across Snowflake, BigQuery, and Redshift
• dbt-based transformation architecture (staging, intermediate, marts)
• Ingestion pipelines design (EL/CDC, batch vs streaming) and data quality integration
• Data cataloging, lineage, and governance for self-serve analytics
• Use Case: deploy a company-wide analytics foundation enabling trusted dashboards and self-serve analytics

Quick Start

Set up a modern data stack by choosing a warehouse, defining a dbt project, and enabling data quality checks for a repeatable, scalable analytics foundation.