Data Engineer

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Montefiore Medical Center is nationally recognized for clinical excellence—breaking new ground in research, training the next generation of healthcare leaders, and delivering science-driven, patient-centered care. Montefiore is ranked among the top hospitals nationally and regionally by U.S. News & World Report. For more than 100 years, we have been innovating new treatments, procedures, and approaches to patient care, producing stellar outcomes, and raising the bar for medical centers in the region and worldwide. As we build on this momentum, we continue to advance the practice of medicine and set the standard for excellence. Our workforce is among the most diverse in the US: Montefiore associates speak 60+ languages.

Position Summary:

The Data Engineer, Research Informatics plays a critical role in enabling data-driven clinical and translational research across Montefiore. This position is responsible for designing, building, and maintaining scalable data pipelines and research-ready datasets that integrate complex, multi-source healthcare and research data, including EHR systems, clinical registries, and external datasets.

Working at the intersection of clinical care and scientific discovery, the Data Engineer develops robust data models and infrastructure to support key research use cases such as cohort identification, longitudinal patient tracking, clinical trials, and real-world evidence generation. This role emphasizes data quality, reproducibility, and compliance with regulatory standards, ensuring that data assets are reliable, secure, and suitable for research use.

The Data Engineer will work closely with clinical researchers, biostatisticians, data scientists, and cross-functional teams to translate complex research and clinical questions into scalable data solutions. In addition, this role will collaborate side by side with data engineers across the broader Montefiore data organization, aligning with enterprise data standards, platforms, and best practices to ensure consistency, scalability, and interoperability across clinical, operational, and research domains.

This role also contributes to the advancement of enterprise research data platforms, supporting secure data access, governance, and innovation in analytics to improve patient outcomes and accelerate scientific discovery.

Responsibilities:

• Design, build, and automate robust, scalable data pipelines for ingesting, transforming, and loading both structured and unstructured data from diverse internal and external sources, including EHR systems (e.g., Epic Clarity/Caboodle), clinical research systems, registries, and third-party research datasets
• Design, develop, and support data models and schemas to enable integration of clinical and research data, supporting use cases such as cohort identification, longitudinal patient tracking, and research analytics
• Implement CI/CD pipelines and best practices for data engineering assets, ensuring reproducibility, version control, and reliable deployment of research data pipelines and datasets
• Design and implement rules-driven data quality frameworks that enhance observability, transparency, and auditability of research data, supporting compliance with regulatory and research standards (e.g., HIPAA, IRB protocols), and enabling rapid identification and remediation of data issues
• Develop and maintain research-ready datasets that support clinical trials, observational studies, population health research, and real-world evidence generation
• Integrate and harmonize data across heterogeneous sources, including structured EHR data, unstructured clinical notes, imaging metadata, genomics, and patient-reported outcomes, to enable advanced analytics and data science workflows
• Collaborate with product managers, clinical researchers, biostatisticians, data scientists, BI developers, and application teams to understand research and clinical questions, and translate them into scalable, high-quality data solutions
• Support secure data access, governance, and de-identification processes to enable compliant use of data for research purposes while protecting patient privacy
• Contribute to the development and enhancement of enterprise research data platforms and data products that accelerate scientific discovery and improve patient outcomes

Required:

• 5+ years of experience in data engineering, with a focus on building and supporting scalable data pipelines and modern data architectures
• Strong proficiency in SQL
• Experience with Snowflake (will consider similar cloud data platforms)
• Experience with ELT/ETL tools such as dbt, Matillion, or similar frameworks; proficiency in Python for data transformation and pipeline development
• Experience with AWS (S3, compute services), Git, and DevOps workflows
• Understanding of data privacy, security, and regulatory considerations in a research environment (HIPAA, IRB, data use agreements)
• Bachelor’s degree in Computer Science, Engineering, Biomedical Informatics, or a related field

Preferred:

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

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