My most recent work has been in healthcare SaaS, where cloud-native architecture fundamentally changes what integration at scale looks like. At Cedar, I built ETL pipelines orchestrated with Apache Airflow DAGs, ingesting and transforming enterprise client data across AWS S3, Fargate, and ECS, with infrastructure provisioned end-to-end through Terraform. The SaaS model demanded integrations that were multi-tenant, configurable, and fully observable; by shifting to a manifest-based approach, we cut time-to-market from six months to four weeks per integration, while maintaining a 100% integration health score processing millions of records monthly with zero data quality incidents.
At Dental Link Exchange, I took ownership of the full AWS stack from the ground up: VPC networking and security groups through API Gateway, Lambda-based workers, SQS queues, and RDS Aurora, wired into an event-driven file processing pipeline handling thousands of dental transactions daily at 99.9% accuracy. I also built a three-model ML scoring system using LightGBM with Bayesian model comparison to validate training improvements, covering risk prediction, production forecasting, and timeline estimation, reducing manual review workload by 70%. The backend was designed around DDD with vertical slice architecture and CQRS, using X12 EDI standards for data interchange and AES-256 encryption across the infrastructure.