Clinical Data Engineer
US$129,000 – US$145,000 / yr base salary
Posted 15 Oct 2026
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- Remote
- Contract
- Full-time
| Base salary | US$129,000 – US$145,000 / yr |
|---|
About Viz.ai
Viz.ai is the leader in building and deploying AI-powered Care Pathways and helping doctors do their work. The Viz Platform is deployed in 2,000 hospitals across the United States and trusted by many of the leading life sciences companies. The platform uniquely combines real-time, multimodal clinical data with deep clinician engagement to detect disease earlier, coordinate care teams, and help ensure patients receive the right treatment faster. Viz.ai was the first company to be awarded CMS reimbursement for AI and is ranked the #1 Healthcare AI Platform by hospitals and health systems in the Black Book Research survey. For more information, visit Viz.ai.
Role Overview
Viz.ai’s AI-powered care coordination platform helps care teams get patients to the right treatment faster. Our Evidence Generation team shows, with rigorous real-world data, how that translates into better outcomes for patients, providers, health systems, and sponsors.
You will build the data foundation for that evidence. You’ll design automated pipelines that turn raw, messy EHR data into clean, audit-ready research datasets, and you’ll own the quality of those datasets from first ingest through data lock. If you like solving hard data-mapping problems and care that the answer is provably correct, this is a rare chance to do it where the results reach real patients.
Key Responsibilities:
Health Data Engineering & Automated Pipelines
-
Design and maintain version-controlled Python/SQL pipelines that transform raw EHR extracts (FHIR bundles, HL7v2 messages, flat files) into structured, EDC-ready datasets, handling field mapping, unit harmonization, deduplication, and complex derivation logic.
-
Reconcile disparate data models, adapting to site-specific EHR variability, mapping constraints, and schema changes.
-
Apply LLM and NLP techniques to extract structured clinical variables from unstructured sources (clinical notes, pathology and radiology reports, discharge summaries), and validate their accuracy.
Clinical Data Management (CDM) & Quality Control
-
Drive end-to-end data management for Evidence Generation studies, spanning data ingestion, cleaning, quality control (QC), and data lock.
-
Implement programmatic QC workflows to perform automated anomaly detection, field mapping validation, query generation, and audit trail checks, significantly reducing manual review burden.