Ravikiran Patil

Experienced Senior Software Engineer specialized as an Azure Data Engineer with a proven track record in transforming enterprise reporting and analytics. Adept at analyzing functional requirements, developing SQL code, optimizing data workflows, and conducting thorough data validations. Strong background in Azure Data Factory, Azure Synapse, Azure Data Lake, and SQL Server, with hands-on experience in project management, client interactions, and ensuring high-quality deliverables.

• Hewlett Packard - Transforming Enterprise Reporting & Analytics (HP-TERA), HP-TERA is a cloud based project where we move HP related data to cloud platform for which extensively used various technologies like Hadoop, Hive, Spark, Talend, and Azure. These things will be handled in three different layers: Ingestion, Distillation, and Consumption. Ingestion will get the data from different sources which is cleansed using Hive and moved to distillation. Distillation will prep the data and drop the files to Azure data lake storage using Talend. Consumption loads data into Azure Synapse Analytics, Azure Data Base through Azure Data Factory and also creates complex merge tables and metrics using SQL as required for reports. Final job is to create tabular models and report generation as per the Business requirements using PowerBI.
• Manitoba Public Insurance (MPI), MPI is a public insurance company aiming to improve its business through data insights. The project uses Azure cloud platform for data storage, transformation, and cleaning. Data is sourced from the CRM system and Celtic source fetched using Synapse Link, and transferred to ADLS Gen2. Pyspark notebooks clean and transform the data, which is then loaded into SQL Server. Power BI generates reports and insights using the SQLServer tables. This project enables MPI to make informed decisions and enhance their business operations.
• RAC, RAC is automobile insurance company aiming to enhance business operations through data insights using the Azure cloud platform. Data was sourced from D365 systems,API, XMLsources and various databases, ingested using Azure Synapse Pipelines, and transferred to Azure Data Lake Gen2 (ADLS Gen2). Utilized PySpark notebooks for cleaning and transforming data in Delta Lake. The data processing workflow included landing, raw, integration, and data product zones, ensuring data quality and enrichment. Processed data was stored in SQL Serverless Pool, with SQL views created for reporting. Power BI was used to generate interactive reports and insights based on the processed data. This solution improved data integration efficiency, enabled real-time analytics, and supported informed decision-making for the insurance company.
• Cloud Springboard Programme'22-23, Azure Data factory, Azure key vault. Azure data lake Gen2, Azure Logic Apps,Azure SQL Database. Power BI, The project required working with API data for a government Pollution record daily. The data needed to be cleaned and transformed and then statistical aggregation was to be done on the dataset.

• Rising star award for the first half of the year 2021
• Completed Specialization on Data Visualization with Tableau from Course-Era
• Certified with Python programming Essentials from Course-Era
• Completed Microsoft certified certificate of Azure AZ-900