Revenue Forecasting Platform

Challenge

Finance and business teams needed revenue estimates they could use for planning. Existing reports showed what had already happened and did not consistently answer what was likely next.

Approach

I built a forecasting pipeline with historical revenue data, Python, BigQuery, dbt, and Vertex AI. Training, backtesting, and scoring were set up so FP&A could use the outputs in planning.

Architecture

• Historical revenue and operational data landed in BigQuery
• dbt models prepared forecast-ready features and evaluation tables
• Python and Vertex AI handled training, backtesting, and scoring
• Automated retraining and evaluation workflows kept the process current
• Forecast outputs were delivered through governed analytics layers

Implementation

• Defined the forecast grain, validation windows, and success criteria with stakeholders
• Built reusable transformations so model inputs were consistent across runs
• Compared model performance through backtesting before widening the rollout
• Expanded forecasting toward business verticals after the core workflow proved stable

Result

• Approximately 98% backtesting accuracy
• Approximately 97% accuracy during subsequent validation
• Expanded forecasting toward business verticals
• Automated retraining and evaluation workflows