Platforms and experiences that move data to insight.

This project implements an ETL pipeline to process historical bike sharing data from Austin (2013–present). The goal is to enable stakeholders to make informed decisions on bike usage patterns, station performance, demand distribution, and vehicle/subscription trends. The pipeline extracts raw trip data, transforms it to calculate useful metrics (e.g., trips per kiosk, trips per hour, top stations, subscription and bike type analysis), and loads the results into a format suitable for visualization and analysis.

Scenario planning workbench that helps finance and product teams test assumptions with explainable ML forecasts.

Centralised metrics platform standardising KPIs and refreshing executive scorecards in minutes instead of hours.