About Forward-Deployed Engineering at QH. Data Modernization is a forward-deployed function. Every role on this team — leadership and IC alike — works directly with health system customers, on-site and in their environments, throughout the engagement. This is not a back-office data role: you’ll sit with the customer’s data and IT teams, present your work to their technical leadership, and be accountable for outcomes they can see. All roles are Senior/Staff level or higher.
Note on platform specialization: We are hiring Senior / Staff Data Engineers who each own deep expertise in one of our three target platforms — Databricks, Snowflake, or Microsoft Fabric. This single posting covers all three seats; we’ll match you to the platform where your depth is strongest during the process. The core of the role — landing health system data in a modern lakehouse and serving as the platform-specific technical lead on an engagement — is the same across all three.
This is the role where the data actually moves. As a Senior Forward Deployed Engineer on the Data Modernization team, you own the platform-specific build that takes a health system from legacy connectivity — flat files, manual SFTP, a half-finished Clarity database — to a modern, AI-ready lakehouse that can serve our agentic AI workflows at full speed.
You are the deep platform expert for your stack. During an active engagement, you serve as the platform-specific technical lead under the Principal Solutions Architect: you own the ingestion, the medallion architecture, the governance configuration, and the data-sharing pattern on your platform. Between engagements, you sustain our production environments, build the accelerators and reusable IP that make the next engagement faster, support pre-sales technical discovery, and cross-train on the other platforms so the team stays flexible.
These are time-boxed, high-stakes builds. A greenfield foundation goes from zero to a live AI workflow in roughly ten weeks; an acceleration engagement folds hundreds of Clarity tables into an existing lakehouse on weeks-to-months timelines. You’ll ship production-grade work in a regulated environment, where “done” means it’s governed, documented, and ready to hand to the integration team — not just that the pipeline ran once.