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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Clinical Data Engineer - **Company:** Eight Sleep - **Location:** United States - **Experience:** Experienced - **Salary:** $110,000.0 - $130,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, Biometrics, Clinical Data Repository, Information Engineering, Data Integrity, Extract Transform Load (ETL), Cursor (Graphical User Interface Elements), Database Queries, Software Debugging, Firmware, Python (Programming Language), Machine Learning, NumPy, Signal Processing, SQL Databases, Management of Software Versions, Supervised Learning, Data Storage Technologies, Snowflake, Hardware Testing, Pandas, Build Tools, Software Coding, GPT, Data Pipelines - **Published:** September 11, 2026 - **Apply:** https://www.thejobnetwork.com/job/57be9098-4179-48fa-a695-13ca0b6d2a51/clinical-data-engineer ## About the Role We're here to build fast, push limits, and deliver without compromise. If you thrive under pressure and want to do the most meaningful work of your career, you'll feel right at home. If you're looking for something easier - this isn't it., * 2+ years of data engineering 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 * Experience integrating internal and third-party APIs into unified data pipelines * Strong cross-functional communication skills - ability to translate complex analyses into clear insights for non-technical stakeholders ## Description We're looking for a Clinical Data Engineer who will own the end-to-end data pipelines for our clinical studies, including regulatory trials. 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. You'll be the connective tissue between our internal teams and the external clinical sites, and can continuously think outside the box to make our studies more efficient for the research associates and data scientists. You will operate at the intersection of data engineering, applied data science, and clinical research by working directly with raw sensor data from all of our products, along with the ground truth data to validate our algorithms to make key product decisions. You will own the end-to-end data lifecycle, from ingestion to analysis to communication. This role is highly cross-functional with hardware, software, product, and research teams. 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 and analyses * Leverage AI-assisted tools to accelerate data analysis, debugging, and code development, improving iteration speed and reducing manual effort Clinical Analytics & Algorithm Validation * Analyze sleep, physiological, and behavioral datasets to evaluate product performance and validate new features * Perform statistical analyses (e.g., correlation, error metrics, bootstrapping, validation frameworks) to assess algorithm accuracy and clinical outcomes * Develop evaluation pipelines for metrics like HR/HRV accuracy, presence detection, and sleep staging * Build tools and structured datasets to support training and validation of machine learning models, integrating multiple data sources for supervised learning * Investigate edge cases, sensor issues, and data anomalies to improve model robustness Internal Tooling & Visualization * Maintain and extend Python-based applications for visualizing and annotating biometric data * Develop interactive tools for researchers and engineers to inspect sessions, validate signals, and debug algorithms * Streamline workflows for clinical teams to reduce manual effort and improve reproducibility Cross-Functional Collaboration & Communication * Partner with Machine Learning, Hardware, Firmware, and Product teams to build algorithms and test prototypes * Work with Growth and Product teams to explore user behavior and inform feature development * Synthesize findings into reports, dashboards, and presentations for internal teams and external audiences * Contribute to abstracts, posters, and conference presentations; communicate uncertainty, methodology, and tradeoffs clearly to guide decision-making ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Vectorize all the things! 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