2,846-line Python ETL pipeline generating Board-level delinquency dashboards with zero manual intervention.
The most technically sophisticated automation in the portfolio. Nick built a production-grade Python ETL pipeline that extracts data from complex multi-sheet Excel workbooks, applies 12 validation checks, generates 35-worksheet output files, and produces Board-ready interactive HTML dashboards — all with zero manual intervention.
The DQ Engine v3.1 processes 313,493 source cells from production Excel workbooks, performing data extraction, transformation, validation, and visualization in a single execution. The 12 automated validation checks catch data quality issues before they reach stakeholder-facing reports, maintaining 100% MIAC submission quality scores.
Output includes interactive HTML dashboards with Chart.js visualizations featuring DPD distribution analysis, delinquency bucket migration tracking, vintage cohort analysis, and credit tier segmentation. The dashboards were distributed directly to the Board of Directors and PeakSpan Capital for Series B due diligence preparation.