RAKESH H V

With a proven track record at Publicis Sapient, I am a seasoned Data Engineer specializing in leveraging Python and PySpark to architect and optimize data pipelines and workflows. My expertise in Azure cloud services and a knack for problem-solving have driven efficiency improvements by over 30%, showcasing my technical acumen and innovative approach.

Project titel: Track and Trace (Janson & Jansons)

Description: 

Helped in developing Set Track and Trace app for real-time location tracking of high-value medical assets, implementing data engineering techniques and technologies, including Azure Data Lake, Apache Spark, Databricks, Bitbucket, and PySpark for data processing and analytics within the project. Contributing to data driven decision-making by providing insights from the project data.

• Create Pipelines to extract data from the database, transform it if necessary, and load it into the ADLS.
• Optimize queries and data processing pipelines for performance.
• Collaborates with database administrators and cloud architects to optimize data transfer efficiency and minimize downtime.
• Developed some Spark-SQL Scripts for performing the transformations in Databricks .
• creating frame work for Autoloading and validation for data.

Project titel: UHC (Optum RX)

Description:

The project involves migrating data from a project that provides DURG and Claims management solutions where they are tracking the prescription drug for person and track drug availability near the shops and tracking medical claims for the patient. From on-premises to Netezza. Additional AZCopys are created to support the UI system, ensuring continuity in functionalities such as reporting. This migration facilitates scalability and performance improvements. 

• Involved in loading data from Netezza server to Azure Cloud.
• Ingesting the data from the source system using Data bricks.
• Applying High level aggregations like extract, Pivot, transform on the data frame Written Spark data frame UDF and used them in spark SQL and writing cloud formation templates in JSON.
• Primarily involved in Data Migration using Netezza, Azure Data Lake,and Azure Data Factory.
• Responsible for splitting table data in raw container load that data into parts and running those parts in data bricks notebook.
• Responsible for running notebook to converting csv data into parquet format.
• Collecting all the required details to onboard new data sources into the environment Preparing Metadata entries for the new data sources.
• Writing the business logic for multiple instances and for the code retrieval and for the storing purpose.
• Developed some Spark-SQL Scripts for performing the transformations in Azure Data Bricks.
• Responsible for running notebook to load table into delta table and test and compare data counts.
• Applying High level aggregations like extract, Pivot, transform on the data frame Written Spark data frame UDF and used them in spark SQL.

Project titel: OPW Leap Reporting

Description:

 OPW (One Petroleum Way) is a company that provides fuel management solutions to the retail fuel industry. Leap Reporting is a software platform developed by OPW that allows their customers to manage and monitor their fueling operations. OPW is likely using Azure to host and deploy their Leap Reporting platform, which is used by their customers to manage their fueling operations.

• Utilize Azure Data Factory to orchestrate the data migration process.
• Set up Linked Services to connect to the existing Database and Azure services.
• Create Pipelines to extract data from the database, transform it if necessary, and load it into the ADLS.
• Optimize queries and data processing pipelines for performance.
• Collaborates with database administrators and cloud architects to optimize data transfer efficiency and minimize downtime.
• Developed some Spark-SQL Scripts for performing the transformations in Azure

• English
• Kannada
• Telugu

• Microsoft Azure DP-203 Certification
• Demonstrates an expert’s ability to design and implement big data analytics solutions using Azure data Services.