Jobs and Careers

Senior Data Engineer

United States (Remote), United StatesRemotefull_timeVerifiedPosted 18 Jun 2026

About the role

About Versapay 🚀

Versapay turns accounts receivable (AR) into a competitive advantage.

Inefficient AR processes slow cash flow and stall growth. Versapay removes friction, unlocks working capital, and accelerates momentum — giving finance leaders the clarity and control they need to drive business forward.

Versapay automates accounts receivable, removing barriers to collecting and reconciling B2B payments. Our solutions connect finance teams, customers, and business systems in one ecosystem to ensure cash flow clarity. With over 10,000 customers and 5M+ companies transacting on the platform, Versapay processes over 110M transactions and $257B annually.

Think you might be the next Veep to join? Read on!!

Here’s how you’ll make a huge impact here – and on your career:

The Analytics team is evolving our enterprise capabilities from foundational governance into a robust data platform, safely accelerating strategic AI enablement and delivering high-margin commercial data products. As a Senior Data Engineer, you will be pivotal in optimizing and scaling our foundational Snowflake architecture while aggressively pushing toward agentic engineering and machine learning operations. You will operate as a full-stack generalist within the engineering pod, sharing cross-functional responsibility for pipeline resilience, advanced observability, and the deployment of intelligent semantic models that directly feed our product ecosystem.

Reports To: Manager of Data Engineering

What You'll Do:

Architect for the Future: Optimize our existing Snowflake architecture, establishing strict environmental isolation and scalable structures that prepare our data for eventual downstream commercialization and product offerings.

Drive Agentic Engineering: Leverage tools like Snowflake Cortex, Cursor, and UiPath to automate workflows, build semantic models, and deploy agents that accelerate time-to-value.

Establish Data Observability: Implement and manage robust data quality and observability frameworks to ensure pipeline reliability and proactive issue resolution.

Operationalize Machine Learning: Design and maintain MLOps pipelines to support the seamless rollout, monitoring, and lifecycle management of ML models directly within Snowflake.

Execute Shared Ownership: Partner closely with your peers under the Data Engineering Manager to share responsibilities across pipeline management, MLOps, and architecture, avoiding siloed knowledge and ensuring comprehensive team coverage.

Model for Enterprise Utility: Synthesize disparate operational entities into a unified, enterprise-wide semantic model that supports both internal analytics and future data monetization efforts.

Qualifications

5+ years of Data Engineering experience with a deep, specialized focus on Snowflake's advanced features (e.g., RBAC, materialized views, dynamic tables, Snowpipe, stored procedures).

Advanced proficiency in SQL and Python, with a strong foundation in applying software engineering best practices to ELT processes.

Observability Expertise: Hands-on experience implementing data observability and monitoring platforms (such as DataDog) to manage data quality at scale.