Software Engineer, Data & Analytics Platform
Bayesian Health, Inc.11 hours ago
Remote, United StatesSenior
Responsibilities
- Build and scale secure data lakehouse and warehouse infrastructure with pipelines supporting analytics and machine learning.
- Improve data modeling, orchestration, data quality, and scalability as products and customers grow.
- Create LLM-powered tools and automation that enable nontechnical teams to obtain insights without writing queries.
- Design pipelines and data models to monitor KPIs and clinical AI model performance across clients and clinical modules.
- Define company-wide product success metrics and ensure the underlying data is trustworthy.
- Collaborate with engineering, data science, clinical, product, and client success teams to translate requirements into technical solutions.
Requirements
- Bachelor’s degree in Computer Science or another relevant technical discipline.
- At least 5 years of experience building scalable, secure data or analytics infrastructure on a cloud platform for live products and customers.
- Proficiency in Python and SQL.
- Deep knowledge of modern data and analytics technologies, including cloud data warehouses, transformation frameworks such as dbt, workflow orchestration tools, and BI tools such as Tableau or QuickSight.
- Experience leveraging LLMs in distributed data processing and analytics systems.
- Strong communication and cross-functional collaboration skills.
- Preferred: experience securely handling PHI/PII, building analytics technology for clinical or health data, and working through ambiguity in a startup.
About Bayesian Health, Inc.
Bayesian Health builds a real-time clinical intelligence platform that integrates with EHRs to detect patient deterioration and surface actionable guidance for clinicians. It sells enterprise software and services to hospitals and health systems, focusing on clinical decision support and workflow integration. The company is privately held and headquartered in New York City, and its platform emphasizes EMR interoperability and deployment across key inpatient use cases.