About

Snehasish R. is an experienced software developer with a strong background in financial services and product industry. He has worked on various projects aimed at improving customer satisfaction by making things faster and better. With proficiency in functional and reactive paradigm, Snehasish is skilled in Java 8, MVC & Spring framework, Distributed Databases (MemSQL and Greenplum) along with Kafka, ElasticSearch and Kibana Stack. He completed his Bachelor of Technology (BTech) with Honors focused in IT from IIIT Allahabad with a CGPI of 9.15. Snehasish is highly interested in solving complex technical/business problems by leveraging distributed systems. He occasionally finds security bugs while pen-testing random android apps e.g. BetterHalf.ai (Did a responsible disclosure). Snehasish has a strong competitive programming background. He has achieved a maximum contest rating of 2011 in LeetCode with a worldwide ranking of ~7K out of ~220K users. He has also achieved a maximum rating of 1423 (Specialist) in CodeForces and a maximum rating of 1665 in CodeChef. In GeeksForGeeks, he achieved 27th rank out of ~1200 contestants in GFG Coding Challenge. Currently, Snehasish is working as an SWE 3 at Google. He led the design to develop and launch a MapReduce Pipeline which generates daily snapshots, acting as a canonical source to analyze, slice, and aggregate moderation decisions for all the User Generated Content in Google Maps. He optimized the pipeline to ingest 40 TB of data and generate snapshots of 1 TB within 2 hours @ p99. He received peer bonuses and kudos from team members as a form of recognition and hired new talent in Google by taking 10+ interviews. Previously, Snehasish has worked as an SDE 2 - Cart Platform Team at Amazon and as a Technical Associate at Goldman Sachs. At Amazon, he reduced the latency of the cart page by 50ms @ p90 by leading the initiative to optimize product-related notifications on Cart Page. At Goldman Sachs, he reduced the latency of the Risk Infra platform used firm-wide by 500% @ p50 by spearheading the initiative to optimize the underlying data store. Snehasish's tech stack includes Software Engineer, Java, Elastic Search, Kafka, AI, MapReduce, Spring, test, SQL, SRE, Android, Web, and infra. With