the client
Redgarden Engineering LLC
Custom data analysis pipelines turning years of operational data into engineering insight, on demand.
the problem
Redgarden Engineering LLC had years of operational data sitting in spreadsheets, PDF reports, and field-team Notes — invaluable for understanding their fleet performance, but unsearchable, unreportable, and effectively invisible to leadership.
Decisions were getting made on gut feel because the cost of pulling a real answer was an analyst-week. They needed self-serve analytics on top of their own data — not another BI tool no one would log in to.
the response
Data infrastructure for an engineering-first team
We built a data pipeline that ingests their operational reports — PDFs, spreadsheets, field notes — normalizes them into a queryable warehouse, and serves a self-serve analytics layer the engineering team uses daily.
The system handles four years of historical data and updates as new reports come in. Asking 'show me failure rate by site by quarter' is now a 30-second query, not a four-day analyst project.
Stack & Integrations:
- Python
- dbt
- BigQuery
- Pandas
- Plotly
- Hex
- Mode
- Airflow
- OCR pipeline
Asking 'show me X by week' used to be an email thread to our analyst. Now it's a 30-second query our engineers run themselves.— VP Engineering, Redgarden Engineering LLC
the results
Of historical operational data ingested.
From kickoff to first dashboard live.
Self-serve queries used weekly.
Of leadership reports now data-backed.