Selected work

One builder, three layers of work.

The clearest way to show what I do: the same ~1,200-lot Central Florida production builder, from ERP migration to lifecycle KPI system to a warehouse-backed daily operations dashboard.

Audited dashboard scope — every number reconciled to the warehouse

1,110

Full job inventory

124

Active construction scope

Jobs in active build

CO'ed, not closed

854

Sales pipeline rows

134

PM units tracked

Phase 1 · Inside the builder

ERP migration, led end-to-end

I led the builder's ERP migration: data architecture, vendor onboarding, plan/option configuration, and financial validation — while owning the purchasing cost structures across ~1,200 lots, so the new system reflected how the business actually buys and builds.

Data architecture and cost code structure
Vendor onboarding and pricing configuration
Operational + financial validation before cutover

Phase 2 · The reporting layer

Lifecycle KPI system over 1,000+ completed homes

I designed and run the KPI system leadership uses — full lifecycle tracking from lot to closing, cost variances, cycle-time risk — with analytics infrastructure covering 1,000+ completed homes.

Lifecycle KPI framework: lot → plan → construction → closing
Cost-per-plan, margin trend, and vendor variance reporting
Executive investment reports for acquisition decisions

Phase 3 · Consulting engagement

BigQuery warehouse + daily operations dashboard

As an independent engagement, I built a BigQuery-backed daily operations dashboard over the builder's job, sales, loan, and property management data — with a full data-quality audit behind every number.

Daily BigQuery snapshot pipeline from ERP and spreadsheet exports
Every dashboard number reconciled against the warehouse
Exception center: 186 exceptions surfaced, 36 flagged P1

Artifacts

Things you can look at.

Interactive sample dashboard

A working builder operations dashboard with lifecycle navigation, filters, and drill-downs — sample data, real interface patterns.

Open the sample

AI data analyst

A natural-language analyst on top of the warehouse: ask in plain English, get the answer with the SQL shown. Read-only, byte-capped, audited.

Shown on request

Data-quality audit framework

Snapshot reconciliation, coverage and duplicate-key checks, freshness indicators, and a written acceptance checklist before any dashboard ships.

Shown on request

Next step

Want this level of rigor on your systems?

Every engagement ends with reconciled numbers, documentation, and a system your team owns — not a deck.

Book a Discovery Call My Background