bayesianhealth - Software Engineer, Data & Analytics Platform
Requirements
• BS in Computer Science or other relevant technical discipline. • 5+ years of experience in building scalable, secure data or analytics infrastructure on a cloud platform (preferably AWS), serving live products and customers. • Proficient in Python and SQL. • Deep knowledge in modern data and analytics technologies, such as cloud-based data warehouses, transformation frameworks (e.g. dbt), workflow orchestration tools, and BI tools like Tableau or Quicksight, and keen ability to integrate with existing infrastructure to enhance capabilities. • Experience in leveraging LLMs in distributed data processing and analytics systems. • Excellent communication skills and a proven ability to collaborate with cross-functional teams (data science, product, clinical) to translate requirements into robust technical solutions • Experience handling sensitive data that contains PHI/PII securely. • Experience building analytics technology for clinical or health data. • Experience handling ambiguity and uncertainty in a startup.
Responsibilities
• You’ll work closely with other engineers, data scientists, clinicians, product managers, and client success partners to turn complex clinical data into reliable, actionable insights. Day to day, that means designing pipelines and data models, building tools that help our teams monitor product performance and investigate clinical cases, and improving the platform as we expand to new products and health systems. Your work will give the company clear visibility into how our AI performs, which directly shapes how we grow our clinical AI offerings and support our clients. • Build the platform's foundations. Develop our data lakehouse and warehouse, with secure, scalable pipelines that bring our data together to power analytics and machine learning. As our products and client base grow, you'll drive improvements in data modeling, orchestration, data quality, and scalability. • Enable self-serve insights with LLMs and automation. Create tools and frameworks that let our Clinical, Client Success, and Product teams get answers from our data without writing queries, from reviewing and flagging clinical cases to uncovering trends and opportunities. • Power performance monitoring for our clinical AI. Design pipelines and data models that automatically track KPIs and model performance across every client and clinical module we support, using real-time EHR data that is messy and heterogeneous. • Set the standard for analytics company-wide. Work with Client Success, Clinical, Product, Data Science, and Engineering to define how we measure product success and make sure the data behind those measures is trustworthy.
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