• Architected an event processing pipeline using Azure Queues and developed a back-end service (Go) to ingest and write thousands of audit events to durable storage per second

• Developed a back-end service (Go) to monitor real-time data replication from primary to secondary storage, achieving a 75% reduction in replication failure alert latency, from 1 hour to 15 minutes

• Refactored AI model runner deployments to more efficiently select jobs for execution, increasing the model execution pipeline resource efficiency by 50%

• Implemented a new Azure-based backup system for GPv2 storage accounts, overcoming the 125x cost barrier associated with using the existing GPv1 backup system

• Identified redundant labels annotated by Epic’s distributed logging library (Typescript), reducing the cost of storing log metadata by 18%

• Provisioned and configured storage resources using IaC (Terraform) for the migration of Epic’s genetic variant dataset exceeding 100TB

• Advised on the Azure CosmosDB (NoSQL) data model for Epic’s URL shortening and redeeming service handling millions of requests per day

• Led meetings with Azure product teams to evaluate beta features, saving 100 hours of development time by adopting upcoming first-party features

• Improved data security by developing a core encryption library (Go) to standardize and consolidate implementations requiring encryption using the XChaCha20-Poly1305 cipher

• Enhanced GitLab CI pipelines to warn and block builds using outdated versions of Go, ensuring projects stay up to date with current releases