Senior Software Engineer, Data Engineering
Omada Health - Remote, USA
Posted May 1, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
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- Salary
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- 401(k) match
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Schedule
- Shift type
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Senior Software Engineer, Data Engineering Remote, USA Omada Health is on a mission to bend the curve of chronic disease. Job overview: We are dedicated to leveraging data to drive strategic decision-making and operational efficiency. Our team is passionate about harnessing the power of data to solve complex problems and deliver impactful insights. We are seeking a highly skilled and motivated Data Engineer to join our team. The ideal candidate will be responsible for designing, building, and maintaining robust data architectures and engineering data models and pipelines. This role will play a critical part in ensuring the integrity, scalability, and performance of our data processing and products. Key Responsibilities: - Data Architecture: Design, develop, and implement scalable, secure, and efficient data solutions that meet the needs of the organization. - Data Modeling: Create and maintain logical and physical data models to support business intelligence, analytics, and reporting requirements. - Pipeline Engineering: Design, build, and optimize ETL (Extract, Transform, Load) processes and data pipelines to ensure smooth and efficient data flow from various sources. - Data Integration: Integrate diverse data sources, including APIs, databases, and third-party data, into a unified data platform. - Performance Optimization: Monitor and optimize the performance of data systems and pipelines to ensure low latency and high throughput. - Data Quality and Governance: Implement data quality checks, validation processes, and governance frameworks to ensure the accuracy and reliability of data. - Collaboration: Partner closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver
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