Realtime Rider Pricing:
•Built realtime rider pricing service for serving prices for ride-sharing product based on origin, destination, time, and business strategy with machine learning models(predicting driver payout and conversion rate) using Dropwizard framework(Java)
•Built pricing plans management system for generating, deploying, deprecating pricing plans for ride-sharing product with ReactJS, Spark, Dropwizard
•Supported migrating demand model for predicting conversion rate from a micro-service to Michelangelo, a Uber-wide machine learning platform
Supply Data Platform:
•Built a micro-service to provide source of truth for driver state change and supply summary to other services including insurance, fleet management, driver earnings, etc.
Realtime Insights:
•Built a Go micro-service that generates driver related business metrics in realtime for market health monitoring using Kafka and Cassandra, supports 200k TPS at peak
Blacklist/Dispatch Integration:
•Built a data backfill pipeline for blocking driver/rider if you gave/received 1-star rating to/from this person with Spark, and Piper(a job-scheduling service)
Geo-triggers:
•Ingesting global drivers' location update and generating events about drivers entering/existing/dwelling in geofences