About

Arpit Anand is a highly skilled Senior Data Engineer with a strong background in Big Data, Hadoop, and Spark. He is currently working as a Big Data SDE 2 at Amazon, where he is responsible for development and release cycle management for EMR Serverless and EKS docker Kubernetes. Arpit has over three years of relevant experience and a wide range of technical skills, including Kafka, Scala, AWS, Docker, Kubernetes, NOSQL, Impala, and Machine Learning. Arpit has worked on various live Big Data projects, starting from data injection to data cleansing and processing. He has experience in Spark, Scala, Hive, Big Data, Hadoop, Cassandra, and Kafka. He has also worked on Kafka and Spark streaming with Scala and Cassandra as NoSQL tools. Arpit has project experience on Java (Core+Advanced) with Hibernate and Struts. Arpit's past work experience includes working as a Senior Data Engineer II at Zee, where he worked over end-to-end data processing for OTT PLATFORMS such as ZEE5 and SONY LIV. He was the product owner over various business layers of data and built a product for large datasets with the help of PySpark, Hadoop, Big Data, Spark Streaming, Kafka, and AWS. He also worked on end-to-end pipeline development using AWS cloud services, including Lambda, Glue, DynamoDB, Kinesis, and SQS. At Oracle, Arpit worked with the Research and Development team and built a product for large datasets with the help of Spark, Hadoop, Big Data, and Spark Streaming. Arpit has a Postgraduate Degree in Data Science from Symbiosis and a Bachelor of Engineering in Computer Science from Burdwan University. He has a wide range of technical skills, including AWS, Spark, Big Data, Kafka, Hadoop, Scala, Kubernetes, Docker, Python, NoSQL, and SQL. He is also proficient in Elastic Search and Kibana. Arpit is a highly motivated and dedicated professional with a strong passion for data engineering and data science.