GCP Modern Data Stack
Warehouse-oriented analytics engineering on BigQuery
The challenge
Analytics teams often inherit SQL logic scattered across tools and environments. That makes trust, testing, and iteration harder. This project turns analytics engineering into a repeatable product flow with clear data contracts and deployment patterns.
How we solved it
- - Provision storage and analytical resources with Terraform
- - Load source data with Python ingestion scripts
- - Transform and test business models in dbt
- - Use CI to validate changes before publishing
Execution story
Terraform provisions GCP resources, Python moves source data, dbt shapes the warehouse models, and GitHub Actions enforces repeatability.
From warehouse work to public narrative
Recruiters and hiring managers rarely care about SQL files in isolation. They care about what the platform enables. This project turns warehouse transformation into a business-facing story about trust, repeatability, and analytical delivery.
Reuse across channels
Because the content model is structured, this project can be linked to both news references and derivative LinkedIn drafts with very little manual work.