The Problem
The same seller or property could arrive through county records, imported CSV files, DealDigger, skip tracing, and Podio. Treating every arrival as a new lead risked duplicate CRM records, repeated skip-trace costs, conflicting contact information, and multiple reps approaching the same seller.
What I Built
A real estate acquisition and intelligence platform connecting property ingestion, owner intelligence, lead scoring, seller acquisition, buyer intelligence, and sales workflows.
How It Works
Ingest and normalize source records. Match against existing entities before creating records. Preserve source history, connect the correct owner and contact, and validate the handoff into Podio and downstream sales workflows.
Technology Stack
Engineering Challenges
A property, its legal owner, and the person a rep should contact are related entities, but they are not always the same thing. Imports and retries also need to avoid creating duplicate work.
Solutions
Match before creating. Merge matched leads while maintaining provenance. Distinguish property ownership from the contact entity, and validate downstream synchronization. These changes make record identity a system concern rather than a cleanup task for reps.
Screenshots / Demo
A space for approved screenshots, a walkthrough, or an architecture diagram. No private project data is shown.
Outcome
The system was improved to match and merge existing leads, retain source history, separate ownership from contact identity, and validate CRM synchronization. Quantified operational results will be added when verified.
What This Demonstrates
Data engineering, identity resolution, provenance, idempotent workflow design, API integration, and sales operations reliability.
Work With Me
If your data lives across spreadsheets, APIs, CRMs, and disconnected systems, I can build the layer that connects it.
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