[Remote] Senior Data Engineer

Note: The job is a remote job and is open to candidates in USA. Versapay is a company that transforms accounts receivable into a competitive advantage by automating processes and enhancing cash flow. They are seeking a Senior Data Engineer to optimize their Snowflake architecture, drive automation, and operationalize machine learning, playing a critical role in the development of their data platform and commercial data products.

Responsibilities

• 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

Skills

• 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
• Hands-on experience implementing data observability and monitoring platforms (such as DataDog) to manage data quality at scale
• Demonstrated experience using AI-assisted development tools (e.g., Cursor, Cortex) and familiarity with MLOps principles for productionalizing machine learning models
• Experience building and maintaining resilient, low-touch data pipelines using modern integration and orchestration tools (e.g., Fivetran, AWS Glue, AWS Lambda)
• Deep domain expertise navigating complex merchant payment ecosystems (e.g., Adyen), operating under rigorous enterprise data governance and security standards
• Proven ability to architect the translation of high-velocity transactional events into highly optimized, columnar analytical architectures
• Direct experience architecting data products for commercialization, external endpoints, or embedded analytics within a SaaS platform

Company Overview

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