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Real estate data is a classic example of “wide data” at scale. In this episode, Paras Doshi, Sr Director of Data & Insights at Opendoor, explains how his team rebuilt their data platform to support pricing homes using 500+ features per transaction across MLS data, third-party sources, and millions of proprietary photos.
The conversation dives into Opendoor’s four intelligence layers for home valuation, the move from fragmented team-owned stacks to a centralized platform, and how deep lineage, monitoring, and data quality became critical as AI turned the default strategy.
The episode also explores what enabled this transformation beyond technology alone: investing in strong data foundations and having leadership that meets teams in the middle, creating the conditions to deliver real business value with AI.
Chapters:
00:00 - Introduction
06:02 - Opendoor’s four layers of intelligence for home valuation
08:59 - Wide data vs. big data: 500+ data points to power home valuation
11:30 - Infrastructure upgrade: consolidating fragmented stacks
26:10 - Default-to-AI strategy using a POC-first approach
34:21 - Top-quartile hiring: talent strategy for 100x engineers
Paras Doshi leads Decision Science and Data Engineering at OpenDoor. His background spans hands-on and leadership data science, analysis, and engineering roles across industries. Paras is a Fellow of the Institute of Analytics, a data science and AI researcher, and an active blogger with 600+ posts and over 2M reads.
Roy Daniel is the Co-Founder & CEO of definity, the optimization and observability platform for the Lakehouse and Spark ecosystem.
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