Prathyusha

Skilled Azure Data Engineer with 3+ years of experience in designing and optimizing data pipelines using Azure Data Factory, SQL, Synapse Analytics, Databricks, and Logic Apps. Proficient in ETL/ELT processes, data modeling, and cloud data architecture. Strong in performance tuning, issue resolution, and delivering clear knowledge transfer (KT) sessions. Adept at translating business needs into scalable, secure, and reliable data solutions.

• AZ-900 certification: Certification showing foundational knowledge of Microsoft Azure services.
• Azure Data Factory for data engineers
• MTA (Microsoft Technology Associate): Certification validating fundamental understanding of Microsoft technology.
• Oxford Achiever Certification: Recognition for exceptional performance and contributions.

OTVF(ocean transport volume forecast)

A web app basically used to analyze and book the containers in well advance for a finished product based on the volumes to move from one plant to another plant. This will leverage the prebooking of containers in case of crisis where the transportation becomes costlier. The web application delivered by the planners from Nestle used to analyze the volumes for the inter-market transfer to give better visibility of demand analysis for routes and volumes for ocean freight through reporting and building underlying logic, assumptions, and structured data, created procedures on Snowflake which will extract data from different stage tables and merge into a single integration table, created storage integrations, stages, file formats, tasks, views on top of int tables in Snowflake, extracted (unloaded) data to blob storage from Snowflake using copy into commands, migration of code components to higher environments using Azure DevOps, implemented copy activity, stored procedure, and many Azure Data Factory pipeline activities, create, edit, and delete data factories and child resources including datasets, linked services, pipelines, triggers, and integration runtimes, implemented logging and error handling mechanisms using execute PL activity, web activity, implemented logic apps for email notifications, delivered KT sessions to the sustain team, provided supporting documents and mapping sheets required by the sustain team, provided support to the sustain team if they face any hurdles, 

• Best Performer recognition: Recognized as the 'Best Performer' for 2 years for significant contributions, demonstrating technical expertise and dedication.
• Client Appreciation: Received multiple client appreciations for delivering high-quality work consistently.

• Primary ETL: The ETL Team uses Data Stage to acquire data in as close to its Native format as possible in a process called Primary ETL. This strategy allows for full audits, inheritance of metadata and ease of querying because the users immediately recognize the data. Similarly, this strategy allows the BI (Business Intelligence) Team to use the source system to reconstruct access rules.
• Executed unit testing and supported integration testing, providing fixes for identified defects.
• Delivered knowledge transfers to multiple teams

• Data Analysis on Movie Ratings: Engaging in data analysis projects utilizing Python for personal development.
• Facial Expression Recognition project: Building systems like a Facial Expression Recognition scoring system using OpenCV.