Staff Data Engineer

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.

About our Data Stack:

• Cloud Provider: AWS

• Database: MySQL, PostgreSQL

• Extract/Load: Fivetran

• Transform: dbt

• Data Warehouse: Snowflake

• BI Visualization: Looker

• Code versioning: Gitlab

• Preferred languages: SQL and Python

• Infrastructure as Code: Terraform

• Data Quality Monitoring: Monte Carlo, RDS Database Insights, and Datadog

Responsibilities:

As a Staff Data Engineer, you will provide technical leadership in the design, development, and evolution of PayNearMe's modern data platform and data products. You will work closely with Product, Engineering, Risk, Finance, Operations, and Analytics teams to model complex payment data into trusted, governed, and reusable data products that power reporting, analytics, operational intelligence, AI, and customer-facing applications.

This role requires deep expertise in enterprise data modeling, modern cloud data engineering, and payment data. You'll lead the design of scalable data models across the Bronze, Silver, and Gold layers of our lakehouse architecture while establishing engineering best practices and mentoring other engineers through technical leadership..

Enterprise Data Modeling

• Lead the design and implementation of scalable data models across Bronze, Silver, and Gold layers
Collaborate with business stakeholders, product managers, and engineering teams to understand business processes and translate them into well-designed analytical data models.

• Design conceptual, logical, and physical data models supporting enterprise reporting, operational analytics, and AI initiatives

• Develop reusable semantic models that provide consistent business definitions and metrics across the organization

• Apply dimensional modeling best practices including:

• Fact and dimension modeling

• Star and snowflake schemas

• Slowly Changing Dimensions (SCD)

• Conformed dimensions

• Semantic layer design

• Ensure data models are scalable, maintainable, performant, and easily consumable

Data Engineering & Data Products

• Design, build, and optimize cloud-native ELT pipelines using dbt, Fivetran, Python, and AWS Airflow

• Build trusted, reusable data products supporting:

• Payment and transaction analytics

• Merchant reporting

• Customer insights

• Financial reporting

• Fraud and risk analytics

• Regulatory reporting

• Capture and transform transactional payment data from operational systems into curated analytical datasets

• Design highly performant incremental data pipelines capable of processing high-volume payment transactions

• Optimize query performance and execution

Payment Data Expertise

• Develop a deep understanding of PayNearMe's payment ecosystem, including payment lifecycle events, settlements, ACH, card processing, client operations, and consumer transactions

• Model complex financial and payment data with a focus on accuracy, reconciliation, auditability, and regulatory compliance

• Partner with domain experts to establish trusted enterprise definitions and business metrics

Technical Leadership

• Serve as a technical leader and trusted advisor across Data Product Engineering initiatives

• Drive engineering standards, reusable design patterns, and best practices for data modeling and pipeline development

• Participate in architecture reviews…