Data Engineer - Research Informatics (JR229580)

ViziRecruiter,LLC.; City of Yonkers (NY); On-site; USD 100,000 - 130,000; Full time

Job summary

ViziRecruiter, LLC. is seeking a Data Engineer, Research Informatics, to design and maintain scalable data pipelines essential for clinical and translational research. This role emphasizes data quality and compliance, integrating complex healthcare data from systems like EHR.

Ideal candidates will have a strong background in data engineering, SQL, and cloud platforms like Snowflake. This position offers an opportunity to contribute to vital research initiatives while supporting patient outcomes.

Qualifications

• 5+ years of experience in data engineering, focusing on scalable data pipelines and modern architectures.
• Experience with HIPAA, IRB, and data use agreements.
• Understanding of data privacy and security in a research environment.

Responsibilities

• Design and build scalable data pipelines for integrating diverse data sources.
• Collaborate with researchers to translate questions into data solutions.
• Implement data quality frameworks to ensure compliance with regulatory standards.

Skills

SQL

Data pipeline development

Python

ELT/ETL tools

Snowflake or similar cloud data platforms

Education

Bachelor’s or Master’s degree in Computer Science, Engineering, Biomedical Informatics, or a related field

Tools

AWS (S3, compute services)

Git

dbt

Matillion

Job description

Introduction

To heal, to teach, to discover and to advance the health of the communities we serve.

To learn more about the “Montefiore Difference” – who we are at Montefiore and all that we have to offer our associates, please click here.

Overview

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.

Requirements

• 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 and experience with Snowflake or similar cloud data platforms.
• 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 or Master’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.