Clinical Data Engineer

Requirements

• As the world’s first sleep fitness company, we’re redefining what it means to be well-rested and building the most advanced hardware, software, and AI technology to make it possible.
• You will create monitoring tools for tracking live data out in the field that can alert the research associates to any issues, work closely with ML/AI to ensure that incoming data are stored in formats that are easily ingestible and clearly labeled, and work to align datasets with multiple incoming sources of data for analysis by our team.
• Additionally, you will own the data analysis for our hardware validation studies (heart rate, heart rate variability, and presence), helping to make key go/no-go decisions for the company.
• HOW YOU'LL CONTRIBUTE Data Engineering & Infrastructure - Build and maintain scalable ETL pipelines using Python, SQL, and APIs to ingest and process large-scale biometric and sensor data - Design data models and workflows that support clinical studies, internal tools, and downstream analytics - Manage data storage, retrieval, and archival systems in AWS, including handling long-term access and data restore workflows - Ensure data integrity, reproducibility, and proper versioning across evolving datasets
• experience with health/physiology data in a research context — you’ve built ETL pipelines around messy, real-world biometric or sensor datasets, not just clean CSVs - Advanced Python and SQL proficiency — Pandas, NumPy, time-series analysis, and production-quality scripting are daily tools, not occasional ones - Intermediate-to-advanced signal processing and biometric data
• experience — you’ve worked directly with heart rate, HRV, sleep staging, or similar physiological signals from wearable or embedded sensors - Intermediate-to-advanced statistical modeling and validation skills — you can design and execute correlation analyses, error metrics, bootstrapping, and validation frameworks independently - Working proficiency with AWS and Snowflake — you’ve built or maintained cloud-based data storage, retrieval, and archival systems, not just queried them BONUS POINTS -
• Experience with clinical or regulatory trial data, familiarity with GCP/ICH guidelines, or prior work supporting FDA submissions - Background in ML model validation or building structured training datasets for supervised learning - Fluency with AI-assisted development tools (Claude, Cursor, ChatGPT, Copilot) as part of your daily workflow - Domain knowledge in sleep science, biometrics, or wearable/embedded sensor data -

Benefits