Clinical Data Engineer
About The Position
Viz.ai’s Evidence Generation team uses rigorous real-world data to demonstrate how its AI-powered care coordination platform affects outcomes for patients, providers, health systems, and sponsors. The Clinical Data Engineer builds the data foundation for this work by designing automated pipelines that convert raw, variable EHR data into clean, audit-ready research datasets and owning dataset quality from initial ingestion through data lock. The role combines health data engineering, clinical data management, quality control, and site data strategy. It involves building reproducible Python and SQL pipelines; adapting to different EHR systems and data models; applying LLM and NLP techniques to clinical text; creating standardized study data structures; and maintaining documentation, provenance, and compliance. The engineer works with site IT and informatics teams, internal stakeholders, and engineering and AI teams in Israel.
Requirements
- At least 5 years of experience in clinical research, real-world evidence (RWE), or health informatics.
- Demonstrated ownership of research database builds, including cleaning, quality control, and data lock.
- Strong SQL and Python skills for building automated ETL pipelines, transformations, and programmatic QC checks.
- Hands-on experience transforming raw EHR extracts, such as FHIR, HL7v2, or flat-file exports, into structured, EDC-ready datasets.
- Familiarity with clinical terminologies and coding systems such as ICD-10, CPT, LOINC, and RxNorm, and with standard health data structures.
- Experience handling data-model variations across different health systems.
- Experience writing and maintaining governance documentation, including Data Management Plans, SOPs, runbooks, and mapping specifications.
- Working knowledge of version control and reproducible-pipeline practices, such as Git, to keep transformation code auditable.
- Familiarity with LLM/NLP tools for extracting structured variables from unstructured clinical text, such as progress notes and radiology or pathology reports.
- Familiarity with regulated-data requirements, including GCP, 21 CFR Part 11, and HIPAA.
Nice To Haves
- Hands-on experience with major EDC platforms such as REDCap, Castor, or Veeva.
- Familiarity with standardized health and clinical-trial data models such as OMOP CDM, CDISC SDTM/ADaM, or PCORnet.
- Understanding of FHIR resource structure and how EHR data is exported.
Responsibilities
- Design and maintain version-controlled Python and SQL pipelines that transform raw EHR extracts—including FHIR bundles, HL7v2 messages, and flat files—into structured, EDC-ready datasets.
- Handle field mapping, unit harmonization, deduplication, and complex derivation logic in data transformations.
- Reconcile disparate data models and adapt to site-specific EHR variability, mapping constraints, and schema changes.
- Apply LLM and NLP techniques to extract structured clinical variables from clinical notes, pathology and radiology reports, and discharge summaries, and validate extraction accuracy.
- Manage Evidence Generation study data end to end, including ingestion, cleaning, quality control, and data lock.
- Implement automated QC workflows for anomaly detection, field-mapping validation, query generation, and audit-trail checks.
- Build and maintain data dictionaries, EDC forms, and eCRF specifications for standardized ingestion and downstream analytics.
- Maintain inspection-ready documentation, data provenance, and compliance throughout the dataset lifecycle.
- Partner with site IT and informatics teams and internal teams to understand EHR constraints and develop feasible study data-capture plans.
- Author and maintain Data Management Plans, SOPs, runbooks, and transformation and mapping specifications.
- Guide internal teams on process optimization and best practices for working with real-world health datasets across care pathways.
- Collaborate with Israel-based engineering and AI teams to align technical operations and pipeline development.
Benefits
- Competitive cash compensation and equity.
- U.S. benefits include medical, dental, and vision coverage, a 401(k), generous vacation, and additional benefits for full-time employees.
- Israel benefits include dental insurance, performance-based bonuses, a Cibus meal allowance, and meals at the office.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
