Staff Software Engineer, Data Platform

Plenful is on a mission to transform healthcare operations through an innovative AI-driven platform. The Staff Software Engineer will own the data platform that powers Plenful’s automation engine, focusing on designing and evolving the core data model and ensuring platform reliability as the customer base grows.

Responsibilities:

• Own the design and evolution of the core data model — domain entities, actions, and the audit trail that governs every automated decision
• Build the data layer that AI agents read from and write to: expressive enough that models can reason over operational context, governed enough for healthcare compliance
• Define how domain knowledge is structured, versioned, and queried — laying the groundwork for increasingly rich context, serving as the platform matures
• Establish data contracts and APIs that give feature teams clean, stable interfaces to build against
• Drive platform reliability as we double our customer base and traffic without degrading performance
• Set technical direction for the data platform in partnership with the product and feature team leads

Requirements:

• 8+ years of professional software engineering experience building backend or data infrastructure in production
• Deep expertise in relational databases: schema design, query performance, data modeling tradeoffs
• Track record designing and evolving data models in complex, growing systems
• Experience operating production systems at scale, including incident response and reliability work
• Hands-on coding ability in backend systems (Python-heavy environment, but language agnostic)
• Strong reliability instincts: observability, testing, data integrity
• Ability to make good decisions with incomplete information — e.g., designing data models when the domain is still being codified, and product requirements are evolving alongside AI capabilities
• Experience in healthcare, fintech, or other compliance-heavy infrastructure environments
• Background at high-throughput infrastructure companies (Stripe, Brex, Notion, or similar)
• Practical understanding of applied AI systems — how models consume structured data, not pure ML research
• Comfort in customer-facing technical discussions