Digital Health Data Engineer

Job Title: Digital Health Data Engineer

Location: Remote (EU-based candidates only)

Duration: 6-12 months contract

We are looking for an outstanding digital health data engineer whose responsibilities will include:

Position Summary: We are seeking a Digital Health Data Engineer with expertise in analysing multimodal time-series data from biosensors (e.g., accelerometer, ECG, PPG, EEG) and developing advanced data pipelines for digital health applications. The ideal candidate will have experience with Python, cloud-native solutions (AWS, Azure, GCP), machine learning, and familiarity with generative AI (Gen AI) and large language models (LLMs), focusing on the development of digital biomarkers. This role will lead data exploration efforts, drive technical innovation, and collaborate across teams to advance healthcare solutions through development of digital biomarkers.

3+ Years exp is required.

Responsibilities:

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

Requirements

Qualifications:
Required:

1. 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.
2. Strong and proficient in Python, with the ability to mentor and assist the team in solving complex Python-related queries.
3. 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.
4. Expertise in SQL, PySpark, and Dask for data engineering and analysis.
5. Proficiency in working with relational and cloud databases, including PostgreSQL and Redshift.
6. Strong background in machine learning, especially for large datasets.
7. Familiarity with healthcare systems, digital health, and cloud technologies (AWS, Azure, GCP, Snowflake).
8. Experience in digital health, physiological signal processing, and bioinformatics.
9. Excellent communication skills for collaborating and presenting technical concepts.

Nice to Have:

1. Experience in multimodal time-series data (e.g., Accelerometer, ECG, PPG, EEG, etc.) from biosensors.

1. Knowledge of GPU computing, high-performance computing, and cloud-native applications.
2. Knowledge of containerization tools like Docker and their application in deploying data workflows.
3. Familiarity with cardiovascular, neuroscience, or epidemiology data
4. Experience in FDA submissions, validation, and working within GxP environments.