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Snowflake Migration & DBT Model Automation (PDP Project)
Project Overview
Challenge
An enterprise customer operating across multiple verticals wanted to migrate legacy workloads from BigQuery to Snowflake, automate transformations with better testing and data quality controls, maintain dual pipeline operations during transition, and reduce manual effort for recurring ETL logic and model testing. Their vision was a modern cloud-native stack with versioned models, Airflow-based orchestration, and full lifecycle data reliability.
Our Solution
We led a full-stack migration program with DBT automation and dual-environment orchestration.
Dual Loading & DAG Design
Built Airflow DAGs to load data into both BigQuery and Snowflake. Ensured consistency through checksum and sample row validation. Designed alert flows for data discrepancies and pipeline failures.
DBT Model Development
Created modular DBT models for data transformations in Snowflake. Applied naming conventions, documentation, and Jinja macros for standardization. Wrote DBT unit tests to validate business rules and model integrity.
Snowflake Optimization & Monitoring
Used Snowpipe for automated file-based ingestion. Implemented Time Travel for rollback and auditing. Scheduled cost-based query execution windows to control spend.
DevOps & Stakeholder Collaboration
Used Git-based versioning and pull requests for DBT models. Provided training for analysts on DBT best practices. Facilitated migration planning across business, IT, and QA teams.
Results
Risk-Free Migration with Zero Downtime
Dual-environment strategy enabled seamless migration from BigQuery to Snowflake without any service interruptions or data loss.
80% Reduction in Data QA Issues
DBT testing framework dramatically reduced data quality issues through automated validation and business rule checks.
20 Hours/Week Saved in Data Prep
Automation eliminated approximately 20 hours per week of manual effort in recurring data preparation and transformation tasks.
Faster Onboarding for New Use Cases
Standardized DBT models and documentation enabled faster onboarding for new use cases and data domains across the organization.
What Our Client Says
This setup helped us deploy confidently — with data tests, rollback support, and full transparency across teams.