Director, Data Product Engineering

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:

We are seeking a strategic and technically accomplished Director, Data Products Engineering to lead the architecture, engineering, and delivery of AI products, data products and scalable data solutions across our fintech and payment processing ecosystem.

This leader will drive the company’s transition toward a product-centric data operating model by building trusted, reusable, scalable, and business-aligned AI/data products that power analytics, operational intelligence, AI/ML initiatives, customer experiences, regulatory reporting, and enterprise decision-making.

The role requires a strong combination of strategic data solution architecture expertise, modern cloud data engineering leadership, and product-thinking. The ideal candidate will lead teams responsible for engineering high-quality AI/data products, designing scalable data architectures, and enabling reliable enterprise data consumption at scale.

The current ecosystem includes:

About our Stack:

• Snowflake

• Dataiku

• dbt

• Fivetran

• Apache Iceberg on Amazon S3

• Looker & LookML

• SQL, Python

• AWS

• MySQL, PostgreSQL

• Gitlab

• Monte Carlo

• Terraform, OpenTofu

• RDS Database Insights and Datadog

This role will partner closely with Product, Engineering, Risk, and Operations teams to define enterprise data strategies, architect scalable data solutions, and operationalize high-value AI/data products that accelerate business growth and innovation.

Enterprise Data Product Leadership

• Collaborate with the Data leadership team on the refinement of our strategy for Data Products Engineering and scalable data product delivery with a focus on enabling/building AI-powered solutions.

• Establish a product-centric operating model for data capabilities, emphasizing:

• Reusable and governed data products with a focus on accelerating AI/data products

• Domain-oriented ownership

• Data contracts and SLAs

• Product lifecycle management

• Discoverability and interoperability

• Standardized business metrics and semantic models

• Partner with business and technology stakeholders to identify, prioritize, and deliver strategic data products aligned to enterprise goals.

• Drive the creation of scalable enterprise data assets supporting:

• Fraud and risk intelligence

• Transaction analytics

• Merchant and customer insights

• Financial and operational reporting

• AI/ML enablement

• Regulatory and compliance requirements

Strategic Data Solution Architecture

• Lead strategic architecture and engineering decisions for domain data solutions and our modern cloud-based analytical AI/data platform expansion

• Design scalable, resilient, and AI-ready data architectures that support high-volume transactional processing and analytical workloads.

• Collaborate with Data team leadership on enterprise standards for:

• Data modeling and semantic design

• ELT/ETL frameworks

• Data orchestration

• Data quality and observability

• Metadata management and lineage

• Data governance and security

• Performance optimization and scalability

• Architect data solutions that enable trusted, near real-time, and self-service access to enterprise data.

• Drive architectural alignment across operational systems, analytics platforms, AI/ML environments, and reporting…