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Omada Health · Remote, USA
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Omada Health is on a mission to bend the curve of chronic disease. We rely on trusted, high-quality data to power intelligent products, personalized member experiences, and data-driven decision making. As machine learning becomes increasingly central to our platform, we're investing in the data foundations that enable scalable model development, experimentation, and production inference. Job overview: We are seeking a Staff Software Engineer, Data Engineering to lead the design and development of the production data platform that powers machine learning across Omada. In this role, you will partner closely with Data Scientists, Applied AI Engineers, Product Engineers, and fellow Data Engineers to identify, design and build trusted, reusable datasets foundations that serve as the foundation for feature engineering, model training, experimentation, and production inference. Rather than building one-off pipelines for individual models, you'll create scalable data products and feature pipelines that enable multiple machine learning use cases while ensuring consistency, reliability, and governance across the ML lifecycle. You will own the technical design of feature datasets—from ingesting raw behavioral, clinical, and operational data through transforming, validating, and publishing production-grade datasets that are reusable across modeling teams. This role is ideal for someone who enjoys solving complex data problems, designing scalable distributed data systems, and enabling machine learning through well-engineered data foundations. Key Responsibilities: Design, build, and maintain reusable feature datasets that support machine learning use cases including personalization, engagement, risk prediction, churn modeling, recommendation systems, and experimentation. Establish self-service foundations that streamline and democratize dataset creation across the data organization. Partner with Data Scientists to translate modeling requirements into production-ready feature pip
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