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Tags: Data Engineering, ETL/ELT, SQL, Data Modeling, Data Quality Testing, dbt, Git
Domain: E-commerce
dbt
Snowflake
● GOAL
● GOAL
Set up and manage a small-scale ELT workflow with dbt, using data transformations, modular SQL models, and version-controlled pipelines to turn raw data into reusable models and business-ready outputs.
● OVERVIEW
● OVERVIEW
This project is based on the Jaffle Shop dataset, that simulates customer orders, payments, and returns. The project is implemented in Snowflake, transforming raw transactional data into clean, analytics-ready tables while applying dbt’s best practices in testing, documentation, and modular modeling.
This approach mirrors real-world analytics engineering: version-controlled transformations, data quality testing, and a layered model structure.
● IMPLEMENTATION STEPS
● IMPLEMENTATION STEPS
This project includes:
Project Setup: Initialized a dbt project structure and organized folders for models, macros, and configuration.
Data Modeling & Templating: Created modular SQL models using Jinja for templates, using a layer-by-layer logic.
Data Quality Testing: Applied generic tests and defined singular test.
Workflow Practices: Implemented a version-controlled loop: write → run → test → document → commit.
Documentation: Wrote documentation and doc blocks and used Copilot to automate the documentation process.
Deployment: Set up a job for deployment.
● IMPACT
● IMPACT
Bridges the gap between raw warehouse tables and business-ready outputs, preparing data for downstream analysis and reporting.
To see the code, please visit my GitHub.
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