Principal Product Manager - Data Products and Analytics

VEHLO•Knoxville, TN

4d•Remote

About The Position

We're looking for a Principal Product Manager to own our customer-facing data, analytics and insights portfolio end to end. Most of our customers buy more than one Vehlo product and then cobble together other solutions to assemble the 360-degree view of their business that we don't yet give them natively. Closing that gap is the starting point, but the bigger opportunity is what we build on top of it. We're not looking for someone to ship more dashboards. More value will come from AI-powered insights that tell an operator what's happening in their business and what to do about it, without them having to know what to ask. At Vehlo, a Principal Product role is senior, highly visible, and hands-on, reporting to the SVP of Product. You won't have direct reports on day one, but that could change as we grow the business. You'll own strategy and set the pace of play while leading through partners in engineering, analytics, customer success and go-to-market. You'll drive the commercial model as well as the product: what it costs us to serve, what belongs in which tier, and what customers pay for.

Requirements

7+ years in product management, including at least 2 years specifically in B2B SaaS data products, reporting, or analytics.
Demonstrated ownership of customer-facing analytics: reporting, dashboards, or embedded analytics that paying customers relied on. Internal BI and executive reporting are a different discipline and won't prepare you for this.
Experience building analytics that span more than one product or module, rather than a reporting tab attached to a single application.
Ownership of a semantic or metric layer across multiple source systems, including the judgment calls when two teams defined the same metric differently in production.
Experience serving both ends of a wide customer range: the very small owner/operator business that will never hire an analyst, and the large sophisticated customer with its own data team, without shipping two disconnected products.
A zero-to-one track record: you've identified an opportunity, validated it, and shipped a product that didn't exist before, not only improved a mature one.
Commercial instinct with evidence: you've priced or packaged an analytics capability, built a cost-to-serve model, or owned a revenue number for a data product.
Enough technical depth to read a schema, write SQL, and evaluate an API contract without waiting to be told whether something is feasible. That said, we're not looking for you to write production code.
Fluency in the unit economics of analytics.
Genuine enthusiasm for the data work itself: modeling it, making it trustworthy, and turning it into decisions customers rely on every day.
Hands-on working style and fluency with AI tooling: you prototype, pull your own data, and use AI to move faster than a product manager could a few years ago.

Nice To Haves

Experience shipping AI-driven product surfaces to customers, including how you handled accuracy, evaluation, user trust, and being wrong in front of a customer.
You've created public-facing benchmarking or aggregate market-intelligence products that are referenced by the industry.
Usage-based or consumption pricing, and cost attribution per customer.
Experience running customer-facing analytics alongside a small analytics engineering team rather than a large data platform organization.

Responsibilities

New AI-powered insight products, built zero to one: surfacing what an operator would never have thought to ask, explaining why a number moved, predicting what's coming, and recommending the next action, delivered inside the workflow rather than in a separate analytics destination.
The premium analytics product line end to end: reporting and dashboard experiences, alerting, natural-language reporting, and meaningful additions to the basic reporting built into individual products.
The cross-product view: one unified analytics experience spanning a customer's entire Vehlo footprint.
Data delivery beyond the dashboard: APIs, MCP surfaces, exports, warehouse sharing, BI connectors, and the access model that governs which customer sees which data.
The semantic layer and the metric definitions inside it, across products that were built separately and don't yet agree with each other.
Pricing, packaging, and unit economics: cost-to-serve per location, what's free because it drives retention, and what's genuinely a paid tier.
Aggregate benchmarking and market-intelligence products built from our repair data, along with the data-rights and aggregation model that makes them defensible.
Data quality and customer trust as measured product outcomes, so we reinforce trust and value in the market.
Align the teams on a governance model so everyone knows what every number in our analytics product means and where it comes from.
Build a cost-to-serve model per location that our finance team accepts.
Meet customers across the full size range and come back with a point of view on what the unified view has to do.
Publish the product strategy: what belongs in the base product, what belongs in a premium tier, what we stop doing, and your first candidates for net-new AI-powered products, with the reasoning behind them.