Principal Data Product Manager

About this role

Company Description

At PayNearMe, we’re on a mission to make paying and getting paid as simple as possible. We build innovative technology that transforms the way businesses and their customers experience payments. Our industry-leading platform, PayXM™, is the first of its kind—designed to manage the entire payment experience from start to finish. Every click, swipe or tap is seamless, fast and secure, helping non-commerce businesses boost customer satisfaction, accelerate payments, and reduce costs.

Our single platform handles it all: cards, ACH, digital wallets such as PayPal, Venmo, Cash App Pay, Apple Pay and Google Pay, and even cash at more than 62,000 retail locations nationwide. Today, thousands of businesses across consumer lending, iGaming and online sports betting, property management, and tolling trust PayNearMe to deliver a payment experience that drives real results.

In September 2025, we raised a $50 million Series E funding round to accelerate our growth.

We’re a team of 300+ employees across 41 states, headquartered in Silicon Valley with satellite offices in Dallas, TX and Holmdel, NJ.

Join us and be part of a team that’s shaping the future of payments—one experience at a time.

Responsibilities:

As Principal Product Manager, Data & Insights, you will define how data powers products, customer experiences, analytics, recommendations, and analytical AI capabilities. You will set the strategy, priorities, and roadmap for how data is captured, modeled, measured, exposed, and leveraged across the platform, turning it into capabilities that improve customer outcomes, sharpen differentiation, and unlock new growth.

The next generation of software products will be differentiated by how effectively they leverage data. This role defines how data becomes a reusable platform capability, powering better products, better customer experiences, and future innovation across PayNearMe.

Data & Insights Strategy

• Define the long-term vision for data as a strategic product and platform capability.

• Identify opportunities to improve products and customer experiences through data.

• Prioritize investments across data products, insights, analytics, recommendations, and analytical AI capabilities.

• Set roadmap priorities that keep data strategy aligned to business goals.

Platform Data Capabilities

• Define reusable data capabilities that scale across products and experiences.

• Drive accessibility, governance, discoverability, and common business definitions for core data assets.

• Establish a consistent approach for modeling, measuring, and exposing data across the platform.

• Enable teams to build on shared data assets rather than one-off solutions.

Customer-Facing Analytics & Recommendations

• Identify and prioritize opportunities for customer-facing insights, recommendations, and analytics experiences.

• Embed data-driven capabilities into existing products.

• Evaluate and pursue new data-driven product opportunities.

Analytical AI & Automation

• Own the roadmap for analytical AI capabilities that turn raw data into decisions.

• Define the data, measurement, and product requirements behind AI-enabled experiences and workflow automation.

• Shape how data improves decision-making, automation, and customer outcomes.

• Translate emerging technology capabilities into real customer and business value.

What Success Looks Like

• Data operates as a reusable platform capability across multiple products and experiences.

• Product teams consistently discover, access, and trust core data assets.

• Data-driven insights and recommendations are embedded within customer experiences.

• Data investments tie directly to customer outcomes and business impact.

• New opportunities for differentiation and growth emerge through data.

• The company has a clear strategy for data across analytics, recommendations, automation, and AI.

Qualifications:

• 10+ years in Product Management, with substantial experience in data-intensive, platform, analytics, or AI-enabled products.

• Track record defining, launching, and scaling products powered by data, analytics, recommendations, or machine learning.

• Deep understanding of how data is collected, modeled, governed, measured, and exposed through products and platforms.

• Command of the technical landscape across data engineering, analytics, machine learning, and software engineering.

• Ability to translate complex data capabilities into simple, valuable customer experiences.

• Experience defining product strategy in environments where data is a core competitive advantage.

• Systems…