About

Anish Nair is a Senior Data Engineer at Media.net with over 5 years of experience in building data-intensive applications and solving architectural and scalability problems. He has worked across different stages of the data pipeline, including acquisition and integration, and is adept at working quickly and efficiently in close collaboration with analytics, engineering, and other stakeholders. Currently, Anish is working at directi as a Senior Data Engineer, leading a team of Data, DevOps, and UI Engineers to create a cluster for Distributed OLAP System. He has reduced Dashboard latency by migrating data to Distributed OLAP System and developed REST API on top of OLAP System for Customer-Facing Application Interface. Additionally, he has developed a Data Alert Service to reduce development time of Alerts, which helped the internal Data Team to create alerts within a few minutes. In the past, Anish has worked as a Data Engineer 2 at directi, where he developed Data Pipelines to process 5 TB/day using Kafka, Hive, and Spark. He also reduced the Delay of Audited Data end-to-end process from 60mins to 5 to 10mins and developed an ETL system to replace an existing data pipeline built on SSIS using Python and SQL Server. Anish has also worked as a Big Data Engineer - Associate Consultant at axionconnect infosolutions pvt limited, where he was responsible for loading data using Flume to HDFS, data pre-processing using Apache Spark, and developing Distributed OCR processing Application using Apache Spark and Tesseract, among other responsibilities. Anish holds a Bachelor of Engineering (BE) in Computer Science from agnel institue and a Diploma in Computer Technology/Computer Systems Technology from shreeram polytechnic, airoli. Anish's tech stack includes Data Engineering, dotNET, Spark, SQL, Integration, Kafka, DevOps, UI, Python, Big Data, ETL, test, ML, Apache Hive, HDFS, Elastic Search, Search, and Flask. With 6.78 years of relevant experience, Anish is a skilled Data Engineer who can effectively solve complex problems and build robust data-intensive applications.