I build decision-support systems that translate unit economics into executive-ready narratives: margin drivers → scenarios → monitoring → recommendation.
I’m a DBA (Finance), retired Special Operations Command Sergeant Major, and business owner. My work focuses on profitability, forecasting, scenario projection, KPI monitoring, and disciplined execution—built with Python/SQL so leaders can move faster with confidence.
If you’re reviewing me for Sr Business Manager / Strategy & Analytics roles, these are the fastest proof points:
-
Concierge Economics Case Study (NEW) — unit economics + margin bridge + scenarios + monitoring plan
https://github.com/brianpenrod/concierge-economics-case-study -
Finance Operator Toolkit — variance bridges + exec memos + flash reports
https://github.com/brianpenrod/finance-operator-toolkit -
C.A.P.E. Forecasting Co-Pilot — forecasting governance + drift/monitoring concepts
https://github.com/brianpenrod/cape-forecasting-copilot -
FP&A Financial Engineering Portfolio — scenario engines (risk simulation / headcount / rolling forecast)
https://github.com/brianpenrod/fpa-financial-engineering
Strategy & analytics artifacts that leadership teams actually use:
- Unit economics and margin drivers (price/volume/mix, cost drivers, throughput)
- Scenario projections (base/upside/downside) with clear assumptions
- Monitoring & impact measurement (KPI definitions, thresholds, cadence, dashboards)
- Executive narrative (options, tradeoffs, recommendation — written for senior leaders)
| Core Engineering | Analytics & BI | Modeling & Planning |
|---|---|---|
https://github.com/brianpenrod/concierge-economics-case-study
Purpose: Demonstrates how I approach economics for a service business:
- Margin drivers + unit economics
- Scenario projections (base/upside/downside)
- Testing & monitoring plan (KPIs, thresholds, cadence)
- Executive memo with options/tradeoffs/recommendation
Output: a tight “economics narrative” that a senior leader can act on.
https://github.com/brianpenrod/finance-operator-toolkit
Purpose: The “last mile” of analytics — turning results into executive action.
Includes: variance bridges (Price/Volume/Mix), decision memos, and reproducible reporting templates.
Output: clean, repeatable narratives and decision artifacts.
https://github.com/brianpenrod/cape-forecasting-copilot
Purpose: Forecasting governance and monitoring concepts applied to business planning.
Focus: challenging assumptions, detecting drift, and improving forecast discipline.
Output: a practical framework for forecast quality and early-warning monitoring.
https://github.com/brianpenrod/fpa-financial-engineering
Purpose: Planning engines that scale beyond spreadsheets.
Modules: scenario modeling, headcount cost modeling, and driver-based rolling forecasts.
Output: structured planning logic that separates assumptions from mechanics.