Carlos León
All projects

Data products

Chicago Taxi Trips vs Weather

End-to-end GCP pipeline combining Chicago taxi trips and weather signals.

Year
2025
Role
Data Engineer
Chicago Taxi Trips vs Weather screenshot

The problem

I wanted a reproducible way to analyze how weather conditions affect taxi trip activity, using a real pipeline rather than an isolated notebook.

What I built

An end-to-end analytics workflow on Google Cloud, including ingestion, infrastructure, transformations, orchestration, testing, and BI output.

  1. 01Weather API
  2. 02Taxi Data
  3. 03Cloud Functions
  4. 04BigQuery
  5. 05dbt
  6. 06Looker Studio

How it's built

  • Terraform provisions the GCP resources and baseline infrastructure.
  • Cloud Functions and scheduled jobs handle ingestion and refresh workflows.
  • BigQuery stores raw and modeled data, with dbt managing transformations.
  • Looker Studio consumes the final models for stakeholder-friendly reporting.

Outcomes

  • Connected ingestion, storage, modeling, and reporting in one reproducible system.
  • Kept the project close to real production patterns with orchestration and CI/CD.

What's next

Add stronger monitoring and freshness checks around ingestion reliability and scheduled pipeline runs.

Stack

  • Google Cloud
  • BigQuery
  • dbt
  • Terraform
  • Cloud Functions
  • Looker Studio
  • GitHub Actions

Next project

NBA Data Pipeline