Staff Data Engineer

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, 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

• 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, conformed dimensions, and 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, and more.
  • 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 and influence technical direction across multiple engineering teams.
  • Mentor engineers through design reviews, code reviews, documentation, and technical coaching.
  • Promote engineering excellence through testing, CI/CD, observability, and infrastructure‑as‑code practices.
• Cross‑Functional Collaboration
  • Partner closely with Product, Engineering, Data Science, Analytics, Finance, Risk, and Operations teams to deliver high‑value data products.
  • Work collaboratively with stakeholders to understand evolving business requirements and translate them into scalable technical solutions.
  • Communicate complex technical concepts clearly to both technical and non‑technical audiences.
  • Foster strong collaboration across teams to improve data quality, governance, and business alignment.

Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Engineering, Mathematics, Statistics, or related field.
• 10+ years of professional experience in Data Engineering, Analytics Engineering, or Data Platform Engineering.
• Deep expertise designing enterprise‑scale data platforms and cloud‑native data architectures.
• Strong experience modeling complex business domains with emphasis on payment, financial, or transactional data.
• Expert knowledge of dimensional modeling, fact and dimension design, star schema design, semantic modeling, and data warehouse architecture.
• Experience designing and maintaining curated Silver and Gold layer data models within modern lakehouse architectures.
• Expert SQL skills with demonstrated experience writing highly performant analytical queries.
• Strong Python programming skills.
• Extensive hands‑on experience with Snowflake, dbt, Fivetran, AWS, GitLab, Looker / LookML.
• Strong understanding of ELT architectures, data quality, metadata management, data lineage, data observability, CI/CD pipelines, and infrastructure‑as‑code.
• Experience building highly reliable, scalable, and maintainable data pipelines.
• Excellent problem‑solving and diagnostic skills.
• Exceptional written and verbal communication skills.
• Demonstrated ability to influence technical direction without direct management responsibility.
• Strong organizational and communication skills.

Preferred Qualifications

• Experience within fintech, payment processing, banking, or regulated financial services.
• Experience with Apache Iceberg and modern lakehouse architectures.
• Familiarity with Dataiku, Monte Carlo, and Terraform/OpenTofu.
• Experience designing data products supporting AI and machine learning initiatives.
• Knowledge of PCI DSS, SOC 2, and financial data governance requirements.
• Experience implementing semantic layers and enterprise business metrics.
• Experience mentoring engineers and driving engineering standards across teams.

Annual Salary Range

$195,000 – $225,000 USD

Why Join Us?

• Competitive salary and benefits with growth‑company options grant
• Fast‑paced and professional work culture
• Stock options with standard startup vesting - 1 year cliff; 4 years total
• $50 monthly communication expense stipend for phone/internet bill
• $250 stipend to enhance your WFH setup
• Reimbursement for peripheral equipment: monitor (up to $400), keyboard and mouse (up to $200)
• Premium medical benefits including vision and dental (100% coverage for employees)
• Company‑sponsored life and disability insurance
• Paid parental bonding leave
• Paid sick leave, jury duty, bereavement
• 401(k) plan
• Flexible Time Off (team members typically take off ~3–4 weeks per year)
• Volunteer Time Off
• 13 scheduled holidays

PayNearMe strives to create a workplace where all employees thrive. Our core values represent who we are today and we take pride in the way we work with each other as well as with our stakeholders. We’re in this together to do the right thing. We deliver real results we are proud of while remaining respectful, transparent, and flexible.

PayNearMe is an equal opportunity employer. We are diligently and thoughtfully working towards cultivating a diverse workforce which in turn enhances our products and services for the communities we serve. Applicants who represent all backgrounds are strongly encouraged to apply.

California Consumer Privacy Act: Applicant Notice

Effective Date: January 1, 2020; Last Reviewed on December 23, 2019.

The Company is providing you with this Notice to inform you about the categories of Personal Information that it collects and maintains about applicants, and the purposes for which this Personal Information is used. Personal Information includes identifiers and professional or employment‑related information such as name, phone number, email address, academic records, current and past employment history, and more. The Company collects this information to evaluate previous job performance, consider applicants for positions, develop a talent pool, conduct applicant surveys, maintain an internal applicant directory, promote the Company as a place to work, and for workforce reporting and analytics trends.