VAMSI ANAMANENI

Customer-Facing Analytics

• Embedded as data engineering specialist on cross-functional team, contributing across infrastructure, ETL pipelines, and analysis; partnered with data producers (Microsoft IDE teams) to scope and define data contracts
• Implemented core Scala/Spark ETL pipelines processing millions of users of GitHub Copilot products, powering customer-facing analytics dashboards
• Built geo-distributed data platform across 7 Azure regions with GDPR-compliant data residency

Analytics & Insights

• Designed a configuration driven product analytics platform serving 65+ products, enabling self-serve metrics across Product, Sales, and Finance; enabling executive trust in dashboards
• Architected custom segments system for 12+ AI products, allowing product teams to self-serve new dimensions without schema changes which accelerated time-to-insight from weeks to hours
• Led cross-functional GTM initiative partnering with product teams across GitHub to define key metrics, instrument telemetry, and deliver leadership dashboards
• Drove Feature Store v1 for Product Qualified Leads, reducing ML model training time by 50%; automated sales pipeline end-to-end, cutting Marketing Ops effort from 3 days/month to 1 hour

Platform & Infrastructure

• Spearheaded Airflow 2.0 migration across all data platform customers, enabling RBAC and improved orchestration; created migration playbooks and documentation
• Optimized critical database snapshot pipelines, reducing processing time by 47% and eliminating daily failures that caused 24+ hour downstream delays

• Owned end-to-end data pipelines for revenue and product metrics serving 100M+ users across BitTorrent products

• Engineered and optimized MapReduce/Spark pipelines to process over 50 million insurance records daily across 2000+ column datasets.