• Delivered 10x improvement in average time to ship code to production, from hours to minutes

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

• Managed a team of 4 engineers developing careers, setting roadmaps, and working with stakeholders

• 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

• Developed the compute platform to handle >300k data processing jobs per day

• 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

• Improved ML model bulk prediction performance by 2250x and model selection performance by 25x

• 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

• Designed a platform for managing the end-to-end lifecycle of asynchronous batch computes

• 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