About

Vishwajeet Dabholkar is an experienced Data Engineer with a passion for building data ingestion platforms and ETL/ELT jobs. He has over 4 years of relevant experience and is skilled in Python, PySpark, SQL, AWS Glue-Lambda, Databricks, Azure Data factory, and REST API. Vishwajeet is currently working at Fresh Gravity as an Analyst, where he is responsible for building an ingestion platform for a product. He uses Databricks and PySpark to extract and load data from multiple sources into the system for data profiling and quality check. Additionally, he has experience working with MemSQL/singlestoreDB and has deployed singlestoreDB on docker in an Azure VM. Prior to his current role, Vishwajeet worked as a Software Engineer at Paytm, where he optimized existing spark jobs for reducing time and cost. He also created a system for the first level of analysis of deviation in records using Python, Pandas, PySPARK, Spark, Azkaban, and Bitbucket. Vishwajeet was also a Big Data Engineer at Lumiq, where he worked with a highly skilled team of data engineers to build a data lake from scratch for an insurance company. They used the AWS cloud platform and employed Python, PySPARK, SQL, SparkSQL for ETL process. They also used AWS glue for their ingestion and transformation jobs and API ingestion using Python, AWS Lambda, and AWS API Gateway. Vishwajeet holds a Bachelor of Engineering in Computer Science and Engineering from D. Y. Patil College of Engineering & Technology, Kasaba Bawada. He is also proficient in AWS, Data Engineering, SQL, Databricks, Azure, AWS Glue, AI, Big Data, Snowflake, Spark, Pandas, Docker, ETL, Software Engineering, Amazon Athena, Data Science, Jupyter, and Integration. In summary, Vishwajeet is a skilled Data Engineer with experience in building data ingestion platforms and ETL/ELT jobs. He is proficient in various programming languages and cloud platforms and has worked with multiple sources of data. He is a Capgemini Tech challenge 2020 PySpark runner up and one of the first ambassadors of SingleStoreDB.