Clinical Data Engineer

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.

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.

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. Build and maintain data dictionaries, EDC forms, and eCRF specifications that support standardized ingestion and downstream analytics. Ensure inspection-ready documentation, maintaining strict data provenance and compliance throughout the dataset lifecycle. Site Strategy, Governance, & Enablement Partner with site IT/informatics and internal teams to navigate EHR system constraints and translate complex site data realities into feasible study data capture plans. Author and maintain foundational clinical governance documents, including Data Management Plans (DMPs), SOPs, runbooks, and transformation/mapping specifications. Guide internal teams on process optimization and best practices for interacting with real-world health datasets across varying care pathways. Collaborate closely with Israel-based engineering and AI teams to align technical operations and pipeline development.   You Will Thrive in This Role If: You enjoy working with raw, complex EHR data and take satisfaction in engineering clean, structured, study-ready datasets out of messy health system extracts. You are strong in Python and SQL, passionate about building automated, reproducible ETL pipelines, and eager to leverage modern LLM/NLP tools to extract structured data from unstructured clinical text. You value clinical rigor and compliance, remaining meticulous about data integrity, audit trails, and maintainable documentation standards (such as DMPs and SOPs). You excel at technical problem-solving, adept at navigating complex field-mapping challenges while balancing immediate study timelines with