Developed platform to branch and sandbox data processing, enabling parallel ML experimentation in a Write-Audit-Publish pattern

Led a zero downtime migration to a new data processing platform resulting in 40x improvement of P95 incremental and backfill runtime

Re-engineered core data model to improve end to end data freshness by 10x

Managed organization wide observability improvements, enabling end-to-end visibility for distributed logs and traces while driving down costs 3x

Scaled the data platform to handle a 100x increase in customers and 1000x increase in data volume

Improved end-to-end customer data onboarding time by 5x

Drove full text search optimizations resulting in a 10x reduction in cost while retaining performance

Decreased prediction API “ready for traffic” time by 90x with Redis

Migrated all health ML cloud, monitoring, and alerting infrastructure to Terraform

Headed development of a model monitoring service

Facilitated technical mentorship and career growth of multiple junior engineers

Optimized ML model training runtime by 2x and ML model training throughput by 3x

Launched a company-wide CI/CD platform that reduced operational costs by 75% and enabled end-to-end Gitops

Coordinated organization-wide Bitbucket to Github migration

Participated in department-wide project design reviews