Description:

We are seeking a Digital Health Data Engineer with expertise in analyzing multimodal time-series data from biosensors such as accelerometers, ECG, PPG, and EEG.
The role involves 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 and large language models, focusing on the development of digital biomarkers.
Responsibilities include designing, building, and maintaining data pipelines to ensure seamless integration and high-performance processing of large-scale datasets.
The engineer will leverage large language models for digital health applications and innovative solutions.
The role requires providing Python expertise to support team members and drive best practices in code quality and development.
The engineer will manage and optimize cloud infrastructure, including databases and Kubernetes clusters.
The position involves driving technical innovation by implementing generative AI technologies and exploring applications in digital health data.
Communication of results through reports, presentations, and documentation is also a key responsibility.

Requirements:

A 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 is required.
Strong proficiency in Python is essential, with the ability to mentor and assist the team in solving complex Python-related queries.
Experience in data visualization for complex datasets, particularly large-scale datasets and time series data, is required, with knowledge of tools such as Tableau or Power BI.
Expertise in SQL, PySpark, and Dask for data engineering and analysis is necessary.
Proficiency in working with relational and cloud databases, including PostgreSQL and Redshift, is required.
A strong background in machine learning, especially for large datasets, is essential.
Familiarity with healthcare systems, digital health, and cloud technologies (AWS, Azure, GCP, Snowflake) is required.
Experience in digital health, physiological signal processing, and bioinformatics is necessary.
Excellent communication skills for collaborating and presenting technical concepts are required.
Nice to have: Experience in multimodal time-series data from biosensors, knowledge of GPU computing and high-performance computing, familiarity with containerization tools like Docker, and experience in FDA submissions and working within GxP environments.

The position offers a contract duration of 6-12 months, providing flexibility in work arrangements.
The role is remote, allowing for a work-life balance and the opportunity to work from anywhere within the EU.
The opportunity to work on innovative projects in the digital health space, contributing to advancements in healthcare solutions.
Collaboration with a team of experts in the field, fostering professional growth and development.
Competitive compensation commensurate with experience and expertise in the field.