The full lifecycle of a set of vertical datasets, which breaks down into three parts:
Dataset development. Define what the dataset is and who it’s for. Source the data (public records, partner licensing, our own extraction and classification). Set the bar for coverage, accuracy, and entity resolution against ZoomInfo’s company and contact graph, and work with our data analysts to hit it. Decide what “done” looks like for a dataset and hold the line on it.
Platform delivery. Partner with data engineering and platform engineering to get datasets out of the warehouse, into the GTM Data Store, and live across every surface: our APIs, MCP, and our applications. Write the requirements, define the use case queries, own the testing, drive the handoffs. This is the stage where most data products die. You’ll make sure ours don’t.
Commercialization. Own pricing and packaging: how a dataset shows up in the price book, what’s licensed in bulk versus consumed per record, what needs supplemental terms, how it bundles with core data and with other vertical datasets. We’re investing more and more in the product-led motion alongside the sales-led one, so this means enabling both: making datasets discoverable and consumable in-product, and building the sales materials our teams need to sell them by vertical: enablement content, data reference guides, use cases, positioning. Then go sell with them.
Work strategic deals. Sit with sales on the accounts that matter. Get in front of customers, understand what they’re actually trying to do with the data, and shape the dataset and the offer to fit. Some of this is presenting. A lot of it is listening and then going back and changing the product.
Build solutions, not just datasets. Figure out how UCC filings and commercial property data become a prospecting workflow for a commercial banker, or how Form 5500 and insurance producer data become a cohesive platform for the benefits and insurance market. Package datasets into something a vertical buyer recognizes as built for them.
Run point across data, engineering, and go-to-market. You’re the person who knows where a dataset stands at every stage and is accountable for moving it forward. That means working with analysts on data quality, with engineering on delivery, with pricing and legal on terms, and with marketing on how it shows up in market.
Prioritize a portfolio. There are more datasets we could build than we can build well. You’ll help decide which ones get investment based on revenue potential, customer pull, and what makes the core data more valuable.
Close the loop. What you learn from customers goes back into what we build next. What you learn from delivery goes back into how we build faster.