Senior Software Engineer, Data & AI Platform
New
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Omada HealthDigital Health
Remote, USAFull-TimeSenior
SalaryCalifornia, New York State and Washington State Base Compensation Ranges: $179,400 - $224,300*, Colorado Base Compensation Ranges: $171,600 - $214,500*
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLCloud ComputingKubernetesData engineeringSparkTerraformGenerative AI
Requirements
- 5+ years of professional software engineering or data engineering experience building scalable data platforms, services, pipelines, or analytics infrastructure.
- Hands-on experience with modern data architectures, including data lakes, lakehouses, warehouses, analytical datastores, and batch or real-time processing systems.
- Strong programming skills in Python and SQL, with experience building production-grade data systems.
- Working knowledge of distributed computing and cloud-native technologies such as Spark, Kubernetes etc.
- Hands-on experience with Infrastructure as Code using Terraform (or a similar tool such as Pulumi, CloudFormation/CDK).
- Experience applying data engineering best practices around data quality, lineage, governance, reliability, observability, and operational excellence.
- Practical experience using Generative AI tools or technologies to improve engineering workflows, automation, analytics, or data platform capabilities.
- Strong problem-solving, communication, and collaboration skills, with the ability to work effectively across technical and non-technical teams.
Responsibilities
- Design, build, and operate scalable Data & AI platform capabilities for analytics, data science, and Generative AI use cases with strong practices around data quality, lineage, observability, security, and governance.
- Develop reusable abstractions, templates, services, and workflows that standardize how teams build and manage data pipelines and data products.
- Partner with Analytics, Applied-AI, Data-engineering and application teams to deliver platform capabilities that support business and clinical decision-making.
- Evaluate and integrate modern data and AI technologies with a focus on scalability, reliability, developer experience, and cost efficiency.
- Apply Generative AI technologies to improve data enablement, documentation, discovery, workflow automation, and platform usability.
- Promote software engineering best practices, including clean code, automated testing, CI/CD, infrastructure-as-code, monitoring, and secure data handling.
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