Data Engineer - Research Informatics

Montefiore Health System • Plaza, ND

5d • $128,000 - $160,000 • Onsite

About The Position

The Data Engineer, Research Informatics enables data-driven clinical and translational research across Montefiore. The role designs, builds, and maintains scalable data pipelines and research-ready datasets integrating complex healthcare and research sources, including EHR systems, clinical registries, and external datasets.

Working across clinical care and scientific discovery, the Data Engineer develops data models and infrastructure for cohort identification, longitudinal patient tracking, clinical trials, and real-world evidence generation. The role emphasizes data quality, reproducibility, and regulatory compliance so data assets are reliable, secure, and suitable for research. It partners with clinical researchers, biostatisticians, data scientists, and enterprise data teams to turn research and clinical questions into scalable solutions aligned with organizational standards and platforms.

The position also supports enterprise research data platforms, secure data access, governance, and analytics innovation intended to improve patient outcomes and advance scientific discovery. Montefiore employees must be available to work on-site at its Yonkers and Bronx, New York locations as business needs require.

Requirements

• Master’s degree or equivalent research or industry experience in AI, computer science, biomedical informatics, or a related field.
• Experience in one or more of the following: medical imaging; clinical NLP or information extraction; large language or vision language models; multimodal learning; clinical or other sensitive research data.
• Experience with GPU or high-performance computing, reproducible model training and inference, containers, SQL, APIs, or research data platforms.
• Familiarity with clinical research workflows, FAIR data practices, common data models, biomedical ontologies, data provenance, human review, or responsible AI assessment.
• Evidence of research communication and user-focused delivery through publications, presentations, open-source work, training, or documented deployed projects.

Nice To Haves

• Experience working with healthcare and/or research data, including EHR systems (Epic Clarity or Caboodle preferred), clinical registries, or real-world data sources.
• Familiarity with research data standards and models such as FHIR or OMOP.

Responsibilities

• Develop and maintain research data pipelines integrating clinical records, text, imaging, and other approved data sources; document data definitions, quality checks, metadata, lineage, and transformations to support FAIR-aligned reuse.
• Help implement standardized, governed data-access workflows and reusable tools for cohort discovery, phenotyping, trial feasibility, recruitment planning, and research analytics; translate investigator needs into scoped requirements and track delivery.
• Develop, benchmark, and validate AI methods, including information extraction, multimodal models, large or vision language models, and interpretable or knowledge-informed approaches when suitable for the use case.
• Turn validated methods into maintainable research services and interfaces with version control, automated tests, monitoring, user documentation, and clear handoff to authorized operational partners.
• Set evaluation plans with investigators and clinical experts, including baselines, validation splits, human review, error analysis, reproducibility checks, and assessment of subgroup performance and potential bias.
• Work with ICTR, the Data Science Institute, and Montefiore data, IT, security, privacy, and oversight partners to implement approved access and governance requirements; prepare technical evidence for review and escalate higher-risk use cases through established channels.
• Support investigators through consultations, demonstrations, training materials, and reusable examples; contribute technical content to publications, grants, and multi-site research collaborations.
• Track service measures such as request turnaround, cohort or feasibility output quality, reuse of methods, model performance, and investigator feedback; report progress and operational issues.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level