Dinesh Raut

Most recently, I worked as a Lead Engineer in a Data Analytics company at Hyderabad. I am looking for better opportunities in the field. I have 3 years of experience in Databricks, 4 years in Postgre-SQL and 5 years in Python. I also have 2 years of experience in Azure Data Factory and Machine Learning. Before joining the IT industry, I have worked in academia as a research-fellow and as a lecturer. As a researcher of Cosmology, I worked on problems that involved Data Analysis and Scientific Computing. The primary programming languages used were Python, C and Fortran. The projects that worked on, got me interested in Data Science and Big Data Analytics. I worked on some Data science projects on my own. I also pursued a course in Big Data Analytics from CDAC-Pune to facilitate the switch to the data analytics industry. I have published 3 papers in reputed Cosmology journals.

• Post migration testing and support, 02/01/24 - 10/31/24, Monitored and supported the HR Data Lake activities of a client company. Worked on post migration file testing and enhancements using Pyspark, Databricks and SQL.
• Building a unified platform for Data Analytics, 07/01/23 - 10/31/23, Involved in loading data from files to delta tables and consolidating data from different delta tables using ADF and Databricks notebooks.
• Building an efficient framework for Reconciliation, 11/01/22 - 06/12/23, Tested the soundness of data migration from Teradata to Azure Databricks using SQL queries and PySpark.
• Creating an optimal Data Analytics pipeline, 06/01/22 - 09/30/22, Created a complete data movement pipeline involving Azure Data Lake Storage, Azure Data Factory, Azure Databricks, Azure Synapse and PowerBI.

National Talent Search Examination, Regional Mathematics Olympiad, National Eligibility Test (Physics) - Rank 1

• Genre Classification based on movie description, 2021, Processed movie data using pySpark and applied various Machine Learning algorithms to classify genres.
• IEEE-CIS Fraud Detection competition on Kaggle, 2020, Segregated fraudulent credit card transactions using a novel approach and achieved a score of 0.75.