Real-time loan data platform for syndicated loan and private credit markets
Versana digitizes agent-bank loan data in real time, giving syndicated loan and private credit participants a centralized view of deal and position details. Its Java/Spring Boot and React/Angular stack, with AG Grid and D3.js for dense data views, runs across Azure, AWS and GCP. Accelerating, engineering-heavy hiring targets technical debt and architecture simplification, alongside observability, test automation and IT security hardening.
Notable leadership hires: Chief of Staff, Technical Lead
Versana is a New York-based fintech founded in 2021 that captures loan-level data directly from agent banks as it changes. The platform centralizes corporate loan details and portfolio positions for participants in syndicated loans, private credit, leveraged finance and investment-grade lending. The privately held company employs 51–200 people and hires in the United States. Its engineering organization builds full-stack web applications on Java, Spring Boot, GraphQL and React, deployed with Docker and Kubernetes.
Versana builds on Java, Spring Boot, GraphQL, React, Angular and TypeScript, deploys with Docker and Kubernetes across Azure, AWS and GCP, and monitors with Datadog, Grafana and Elasticsearch.
Yes. Engineering dominates Versana's open roles, with hiring accelerating and weighted toward senior, lead and manager levels, including a Technical Lead position. All hiring is in the United States.
Versana is building its real-time syndicated loan platform while investing in application performance, full-stack test automation, observability, endpoint security and disaster recovery testing.
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Versana's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.