Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Lead and modernize the healthcare analytics function, build automated client reporting infrastructure, drive claims and member data analysis, implement metric governance and data quality, leverage LLMs/RAG for AI-informed insights, partner with cross-functional teams and clients, and hire and develop a high-performing analytics team while ensuring HIPAA compliance.

Own and develop Provider Servicing backend systems: design scalable, secure Java/Spring Cloud microservices and integrations (Mulesoft), build APIs and data services, adopt AI developer tools, mentor engineers, collaborate cross-functionally, and automate operations to improve SLAs.

Lead and mentor a small product team to define and execute the product vision and roadmap for Core TPA Claims. Own strategy for the claims processing engine, ensure high-fidelity adjudication and payment integrity, collaborate cross-functionally (engineering, design, data, sales, ops), and implement product best practices and metrics to deliver compliant, scalable healthcare products.

Serve as the primary analytics lead for top clients, owning end-to-end reporting and complex claims analyses. Provide SQL mentorship, design scalable solutions with Data Engineering, manage roadmap in Jira/Confluence, deliver high-impact presentations, and lead rapid ad-hoc and audit analyses to inform clinical and financial decisions.

Lead and manage the core Claims processing system, owning development, testing, and maintenance. Partner with Product, Business, Operations, and Support to define and execute the technical roadmap, automate processes, and improve SLAs. Drive adoption of AI-powered developer tools and architect AI-native, agentic solutions. Provide technical and people leadership to build scalable, cloud-native backend services and integrations using Java/Spring Cloud, Kafka, AWS, Kubernetes, PostgreSQL, and BRMS.

Own strategy, roadmap, and delivery of core data products for TPA operations. Lead discovery with stakeholders, translate requirements into data product specs, run Agile ceremonies, manage backlog, define metrics, triage data quality/pipeline issues, and maintain documentation and data dictionaries.