Staff Data Engineer
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AI Summary
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 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
- 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
- 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.
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 to go towards your 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
- 401k plan
- Flexible Time Off (our 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
PayNearMe, Inc. (the “Company”) is providing you with this Notice (“Notice”) to inform you about:
- the categories of Personal Information that the Company collects and maintains about applicants; and
- the purposes for which the Company uses that Personal Information.
For purposes of this Notice, “Personal Information” means information that identifies, relates to, describes, is capable of being associated with, or could reasonably be linked, directly or indirectly with, a natural person that the Company may collect in connection with screening applicants for job openings at the Company.
- Identifiers and Professional or Employment-Related Information. The Company collects identifiers and professional or employment-related information, which may include some or all the following: real name, nickname or alias, postal address, telephone number, e-mail address, membership in professional organizations, professional certifications, language skills, and current and past employment history. The Company collects this Personal Information to evaluate previous job performance and consider applicants for positions, to develop a talent pool and plan for succession, to conduct applicant surveys, to maintain an internal applicant directory and for purposes of identification, to promote the Company as a place to work, and for workforce reporting and data analytics/trend analysis.
- Personal Information Categories from Cal. Civ. Code § 1798.80(e). The Company may collect categories of Personal Information listed in Cal. Civ. Code §1798.80(e), other than those already listed above, (a) to the extent necessary to comply with the Company’s legal obligations, such as to accommodate disabilities; (b) to conduct a direct threat analysis in accordance with the Americans with Disabilities Act and state law; (c) for occupational health and safety compliance and record-keeping; and (d) to respond to an applicant’s medical emergency.
- Characteristics of Protected Classifications Under California or Federal Law. The Company may collect information about race, age, national origin, disability, sex, and veteran status as necessary to comply with legal obligations, including the reporting requirements of the federal Equal Employment Opportunity Act, the federal Office of Contracting Compliance Programs (applicable to government contractors), and California’s Fair Employment and Housing Act. The Company collects this Personal Information for purposes including: to comply with Federal and California law related to accommodation. The Company also collects this category of Personal Information on a purely voluntary basis, except where required by law, and uses the information only in compliance with applicable laws and regulations.
- Education Information. The Company collects education information such as resumes and graduation records. The Company collects this Personal Information to determine suitability for roles, to determine eligibility for training courses, and to assist with professional licensing.
- Profile Data. The Company may collect profile data, including the following: psychological assessments, behavior analyses, or other profiling of its applicants. The Company collects this Personal Information to determine aptitude for certain positions and job assignments as well.
- Background Screening Information. In the event that an applicant is given a formal job offer, the Company collects background screening information prior to hiring, including results of the following types of background screening: criminal history; sex offender registration; motor vehicle records; credit history; employment history; drug testing; and educational history. The Company collects this Personal Information to screen for risks to the Company and its clients, and continued suitability for their jobs and to evaluate applicants for promotions.
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