Rishabh Didwania

● Owned end-to-end design and delivery of clinical workflows (DOT and Outcomes), building scalable microservices for oncology use cases. ● Designed and implemented APIs for AI-driven workflows, enabling a skill-based execution framework in collaboration with AI teams. ● Built data ingestion and materialization pipelines to migrate legacy data, with validation, routing, indexing, and replay-safe processing. ● Built spec-driven microservices with detailed APIs, enrichment logic, and core workflow capabilities ( access control, audit). ● Delivered across backend services, data pipelines, and UX, collaborating with PMs and leadership on scope and timelines in a fast-paced environment. Tech stack used: Java, Micronaut, OpenAPI, Oracle SQL, Oracle semantic data services, Protocol Buffers, Guice.

● Contributed to the development of an Operational Data Store incorporating data quality assurances and data retention protocols; ensured accuracy and consistency. Enhancing operational data accessibility and enabling real-time reporting, handling over 98.88 million events and a peak throughput of 550 transactions per second (TPS). ● Led the development of a self-serve, real-time reporting platform within VEP, reducing dependencies on external tools. This optimized managerial decision-making and streamlined workflows, reducing typical 20-hour weekly report creation time. ● Led the design and implementation of key components within the Availability and Booking System; real-time and batch data pipelines. This enabled the seamless migration of about 30 M expert schedule records with zero downtime and a minimal error rate of 0.0003%, efficiently servicing 24k appointments post-migration. ● Led a redesign of the coverage management system, advocating for real-time data ingestion that addressed current staffing inefficiency, and establishing more effective coverage management system. ● Developed the Appointment Nudge Capability, an innovative feature that improved appointment adherence by providing real-time. ● Collaboration with Product Managers and Architects to deliver high-quality, scalable, multi-tenant services, significantly enhancing user experience and adaptability through improved interface and system capabilities. Tech stack used: Java, Apache Kafka, Apache Flink, PySpark, Amazon(RDS,S3), Camunda BPMN, JavaScript