About

Nitesh Goyal is a BI & Data Engineering Lead with over 5 years of relevant experience in Real Estate and Investment Management. He is skilled in MS Azure, MS Power BI, MS Power Platform, Python, SQL, NLP, and Big Data. Nitesh is an Azure Data Engineer, adept at automating Data Engineering solutions using Python, while deploying pipelines in Azure and saving data in Azure SQL databases. He has developed Azure Data Factory pipelines for moving PowerBI datasets from Azure Data Lake to Azure SQL Databases, taking advantage of CDM of Azure Data Lake. Nitesh has created PowerBI reports & Dataflows for data analysis, some for reviewing the performance of Azure pipelines. He is proficient in Python, R, SQL, JAVA, and C#. His expertise in Cloud Technologies includes Azure Data Factory, Azure Data Lake, Azure SQL Server, Azure Data Catalog, Azure Purview, Azure Container Services, and Azure DataBricks. Nitesh is skilled in software frameworks like Docker, PowerBI, Tortoise SVN, JIRA, Power Shell, Postman, and PowerBI Dataflows. He is also experienced in REST API integration, JSON & XML text normalization. Currently, Nitesh is working as an Azure Data Engineer at ion, where he is constantly providing guidance to other team members. The team follows AGILE methodologies and JIRA KANBAN boards. He is automating and enhancing various drivers in Python to get, normalize, and ingest data from multiple data-sources into ION data-lake. Nitesh is creating PowerBI Reports & Dataflows for providing deep insight into data and has handled extremely sensitive data across business verticals. He is managing different Azure services like Azure Data Lake, Azure Container services, Azure Data Factory pipelines, Azure Data Catalog, Azure Purview, and Azure DataBricks. Nitesh has worked as a Software Engineer at Aricent, where he worked on multiple technologies and platforms. He has also worked as a Subject Matter Expert at Chegg in the CSE field. Nitesh has a Bachelor of Technology in Computer Science and Engineering from Maharaja Agrasen Institute of Technology. He has also completed a Machine Learning Engineer degree from Udacity. His Tech Stack includes Azure, SQL, Data Engineering, Python, NLP, Big Data, Docker, Databricks, Integration, ML, Software Engineer, Java, and Jenkins.