Implemented features for highly-available, low-latency service discovery.

Optimized low-latency trading systems and order engines.

– Integrated manual range splits with automatic range merges and implemented expiration of manual splits.

– Decreased range descriptor cache misses by over 50% in frequent range split and relocate workloads by improving the eviction policy, using logical timestamps to determine staleness, and coalescing requests.

– Implemented a SSTable based snapshot strategy to decrease memory usage and speed up snapshot application.

– Built large-scale, robust data analysis pipelines to serve machine learning models for improving ad asset selection and composition.

– Streamlined and extended pipeline for selecting and enhancing top performing AdGroups, and increased total network revenue by 0.2%.

– Developed dashboards to evaluate performance of models and discover insights for further improvements.

– Scoped out, architected, and implemented highly scalable materialized views in Redshift using Kinesis Data Streams, DynamoDB, and Lambda.

– Used timelabel tags to represent the state of underlying data, update materialized views when dependencies get updated, and handle schedule and view definition changes.

– Built critical APIs and migration scripts to launch a web application for monitoring materialized views and data pipeline performance.