REAL PROJECTS.
REAL RESULTS.
Here is what I actually built for real companies, with real constraints and real deadlines.
Series A SaaS - Automated KPI Reporting
The founder was spending 3+ hours every week pulling numbers from Stripe, HubSpot, and a manual Google Sheets tracker. Every Monday morning was a copy-paste marathon just to know how the business was doing. Different people quoted different numbers for the same metric.
What I BuiltI built dbt data models that unified revenue and pipeline data into a single metrics layer, pulled product usage from an existing CSV export, then wired up an automated pipeline that delivers a formatted KPI summary to Slack every Monday at 8am. Five KPIs - WRR, pipeline coverage, trial conversion, WAU, and net revenue retention - refreshed automatically.
3 hours/week saved for the founder. Consistent metrics across the entire team. No more "where did you get that number?" debates in leadership meetings. The team now starts every Monday aligned on exactly where the business stands.
Growth-Stage SaaS - Data Foundation for AI Readiness
Data was scattered across 8 different tools with no single source of truth. The data team spent most of their time answering ad-hoc questions instead of building anything strategic. Two AI initiatives had already stalled because nobody trusted the underlying data. The CEO wanted AI agents but the foundation was not there.
What I BuiltI started with an AI Readiness Diagnostic to score their current state across 5 dimensions. Then I built the foundation: Terraform-managed cloud infrastructure on GCP, a dbt data warehouse with tested models covering all 8 source systems, and a CI/CD pipeline so their team could maintain and extend it without me. I documented everything in runbooks their engineers could follow.
Clean, tested data foundation covering all 8 source systems. The data team shifted from firefighting to building. Within 3 months of the foundation going live, the company deployed 2 AI agents on top of it - a customer health scoring agent and an automated support triage agent. Both went to production on the first attempt because the data was trustworthy.
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