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Biomedical Data Engineer Jobs (NOW HIRING)

Nicklaus Children's Hospital

Data Engineer

Miami, FL

$104K - $125K/yr

Responsible for data integration, monitoring of data storage, and data distribution across multiple ... Bachelor's Degree in IT, Biomedical Informatics, Computer Science, Engineering or related field ...

Nicklaus Children's Health System

Data Engineer

Miami, FL · On-site

$109K - $131K/yr

Responsible for data integration, monitoring of data storage, and data distribution across multiple ... Bachelor's Degree in IT, Biomedical Informatics, Computer Science, Engineering or related field ...

Axle

Data Scientist II

Rockville, MD

... and data science applications to research centers and healthcare organizations nationally and ... With experts in biomedical science, software engineering, and program management, we focus on ...

Axle

Data Scientist II

Rockville, MD · On-site

... and data science applications to research centers and healthcare organizations nationally and ... With experts in biomedical science, software engineering, and program management, we focus on ...

Axle

Data Scientist II

Rockville, MD

... and data science applications to research centers and healthcare organizations nationally and ... With experts in biomedical science, software engineering, and program management, we focus on ...

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Biomedical Data Engineer information

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$62

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How much do biomedical data engineer jobs pay per hour?

As of Jul 14, 2026, the average hourly pay for biomedical data engineer in the United States is $62.98, according to ZipRecruiter salary data. Most workers in this role earn between $53.61 and $70.91 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Biomedical Data Engineer, and why are they important?

To thrive as a Biomedical Data Engineer, you need strong programming skills (e.g., Python, R), a background in biomedical sciences or bioinformatics, and experience with data modeling and analysis. Familiarity with big data frameworks, cloud platforms, and tools like SQL, Hadoop, and machine learning libraries, as well as relevant certifications, is commonly required. Excellent problem-solving abilities, attention to detail, and effective collaboration with cross-functional teams help you stand out in this role. These skills enable accurate analysis and integration of complex biomedical data, supporting critical healthcare research and innovation.

What are some common challenges faced by Biomedical Data Engineers when integrating clinical data from multiple sources?

Biomedical Data Engineers often encounter challenges related to data heterogeneity when integrating clinical information from diverse sources such as electronic health records, medical imaging systems, and genomic databases. These sources may use different formats, standards, and terminologies, making data cleaning and normalization a complex task. Additionally, ensuring patient privacy and compliance with healthcare regulations adds another layer of complexity. Collaborating with clinicians, data scientists, and IT teams is essential to address these challenges and ensure data is usable for research and decision-making.

What is a Biomedical Data Engineer?

A Biomedical Data Engineer is a professional who designs, develops, and maintains systems for collecting, storing, and analyzing biomedical data. They work at the intersection of healthcare and technology, collaborating with researchers, clinicians, and IT specialists to ensure that medical data is accessible, accurate, and secure. Their work supports medical research, diagnostics, and the development of healthcare solutions by leveraging large datasets, machine learning, and advanced analytics. Biomedical Data Engineers often use programming languages, database management, and data processing tools to handle complex health data from various sources.

What is the difference between Biomedical Data Engineer vs Biomedical Data Analyst?

AspectBiomedical Data EngineerBiomedical Data Analyst
Required CredentialsBachelor's or Master's in Bioinformatics, Computer Science, or related fields; experience with data engineering toolsBachelor's or Master's in Biology, Bioinformatics, or related fields; proficiency in data analysis and visualization
Work EnvironmentDevelops data pipelines, manages databases, and ensures data infrastructure for research and healthcareAnalyzes datasets, creates reports, and interprets data for research or clinical decision-making
Employer & Industry UsageResearch institutions, biotech companies, healthcare providersHospitals, research labs, biotech firms, healthcare organizations

While both roles work with biomedical data, Biomedical Data Engineers focus on building and maintaining data infrastructure, whereas Biomedical Data Analysts interpret and analyze data to support research and clinical decisions.

More about Biomedical Data Engineer jobs
What cities are hiring for Biomedical Data Engineer jobs? Cities with the most Biomedical Data Engineer job openings:
What states have the most Biomedical Data Engineer jobs? States with the most job openings for Biomedical Data Engineer jobs include:
What job categories do people searching Biomedical Data Engineer jobs look for? The top searched job categories for Biomedical Data Engineer jobs are:
Biomedical Data Engineer jobs near you
Infographic showing various Biomedical Data Engineer job openings in the United States as of July 2026, with employment types broken down into 2% Internship, 1% As Needed, 83% Full Time, 13% Part Time, and 1% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $131,001 per year, or $63 per hour.
Research Associate Data Scientist

Research Associate Data Scientist

Cedars Sinai

Los Angeles, CA • On-site

$97K - $133K/yr

Full-time

Posted 21 days ago

Cedars-Sinai rating

8.6

Company rating: 8.6 out of 10

Based on 130 frontline employees who took The Breakroom Quiz

37th of 1,020 rated hospitals

Job description


Research Associate Data Scientist (Cedars-Sinai Medical Center; Los Angeles, CA): Assist with the development, evaluation, and application of computational and statistical methods, including artificial intelligence and machine learning algorithms and software for the analysis of biomedical data. Assist with the presentation and communication of scientific results through laboratory meetings, scientific conferences, and peer-reviewed publications. Create database-to-deployment pipelines for models using the necessary programming languages (primarily Python, R, and C++). Create sustainable data science infrastructure and adheres to data analysis/machine learning best practices. Perform exploratory data analysis to gauge the need for or appropriateness of advanced analytical methods. Work with senior or lead data scientists, research programmers, and principal investigators to identify areas where data science can best be applied to answer biomedical research questions. Test and validate code to ensure robustness of data applications. Perform all other duties as assigned. Participate in the development of innovative algorithms and analytical methods. Participate in the evaluation and interpretation of all analytical methods and results. Participate in the oral and written communication of scientific results including publications.
Minimum requirements: Master's degree or foreign equivalent in Electrical Engineering, Computer Science, Machine Learning, Applied Mathematics, Biomedical Imaging, or related field, plus three (3) years of experience as a Research Associate Data Scientist, Computer Engineer, Biomedical Data Scientist, or related occupation.
Must have experience with the following: Python, C++, and R; developing, testing, validating, and optimizing production-level, version-controlled code (GitHub/GitLab and Azure DevOps) for algorithm development, statistical analysis, and deployment; implementing supervised and unsupervised learning algorithms (random forests, support vector machines, clustering, deep learning), with hands-on expertise training, fine-tuning, and deploying deep learning models using frameworks (PyTorch and TensorFlow), and adapting these methods to biomedical research problems; building end-to-end database-to-deployment pipelines including querying large relational databases (SQL), data cleaning, model training, validation, and deploying models in multiple computing environments; communicating scientific results effectively through peer-reviewed publications, patents, conference presentations, and internal technical reports; working with medical imaging data, including familiarity with industry-standard imaging formats (DICOM), image preprocessing workflows (segmentation, denoising, registration, resampling, and normalization), and use of imaging software libraries (SimpleITK, MONAI, or NiBabel) to prepare data for machine learning analysis; managing, processing, and optimizing large-scale 3D and 4D time-series datasets for deep learning model development on High-Performance Computing (HPC) or cloud-based GPU clusters.
Salary: $97,510 - $133,100 per year
To Apply: Any interested applicant may click on the APPLY NOW button above to apply for this position.
Job Req ID:18558
Responsibilities
  • Assists with the development, evaluation, and/or application of computational and statistical methods including artificial intelligence and machine learning algorithms and software for the analysis of biomedical data.
  • Assists with the presentation and communication of scientific results through laboratory meetings, scientific conferences, and peer-reviewed publications.
  • Creates database-to-deployment pipelines for models using the necessary programming languages (primarily R, Python, SQL, neo4j).
  • Creates sustainable data science infrastructure and adheres to data analysis/machine learning best practices.
  • Performs data cleaning, quality control, and exploratory data analysis to gauge the need for or appropriateness of advanced analytical methods
  • Assists research, senior research, and/or lead research data scientists and principal investigators to identify areas where data science can best be applied to answer biomedical research questions.
  • Tests and validates code to ensure robustness of data applications with version control through GitHub.

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