Digital Health Data Engineer

Summary

Join our team as a Digital Health Data Engineer and contribute to the development of innovative digital health applications. This remote, 6-12 month contract position (EU-based candidates only) requires expertise in analyzing multimodal time-series data from biosensors and developing advanced data pipelines. You will leverage Python, cloud-native solutions (AWS, Azure, GCP), machine learning, and generative AI to create digital biomarkers. The ideal candidate will have a strong background in data engineering, machine learning, and experience with healthcare systems. This role involves leading data exploration, driving technical innovation, and collaborating with cross-functional teams.

Requirements

• Bachelor's degree with at least 5 years of industry experience or a Master’s degree with at least 3 years of industry experience in Computer Science, Data Science, Bioinformatics, or a related quantitative field
• Strong and proficient in Python, with the ability to mentor and assist the team in solving complex Python-related queries
• Experience in data visualization for complex datasets, especially of large-scale datasets and time series data, with a strong understanding of tools and techniques such as Tableau, Power BI, or similar platforms for presenting insights effectively
• Expertise in SQL, PySpark, and Dask for data engineering and analysis
• Proficiency in working with relational and cloud databases, including PostgreSQL and Redshift
• Strong background in machine learning, especially for large datasets
• Familiarity with healthcare systems, digital health, and cloud technologies (AWS, Azure, GCP, Snowflake)
• Experience in digital health, physiological signal processing, and bioinformatics
• Excellent communication skills for collaborating and presenting technical concepts

Responsibilities

• Design, build, and maintain data pipelines, ensuring seamless integration and high-performance processing of large-scale datasets
• Using large language models (LLMs) and foundation models, leveraging their capabilities for digital health applications and innovative solutions
• Provide Python expertise, supporting team members with queries and troubleshooting, while driving best practices in code quality and development
• Manage and optimize cloud infrastructure (AWS and Azure), including databases, Kubernetes clusters, AWS Bedrock, Athena, and S3 integration
• Drive technical innovation by implementing generative AI technologies such as RAG (Retrieval-Augmented Generation) and exploring applications in digital health data
• Communicate results through reports, presentations, and documentation

Preferred Qualifications

• Experience in multimodal time-series data (e.g., Accelerometer, ECG, PPG, EEG, etc.) from biosensors
• Knowledge of GPU computing, high-performance computing, and cloud-native applications
• Knowledge of containerization tools like Docker and their application in deploying data workflows
• Familiarity with cardiovascular, neuroscience, or epidemiology data
• Experience in FDA submissions, validation, and working within GxP environments