•Designed and implemented end-to-end ETL pipelines for data migration using Azure Data Factory and Databricks resulting in a 35% reduction in processing time for Unilever.•Configured triggers and schedules within Azure Data Factory to automate the execution of ETL pipelines.•Led two teams which included assigning tasks, providing guidance, and ensuring timely delivery of projects and have collaborated with cross-functional teams.•Focused on Physical and logical data modeling, scalable data architectures and Framework Design for Historical, Incremental Loads and Orchestration.•Worked alongside data visualization team to build dashboards and executed automated PowerBI dataset refresh using components of Azure Data Factory.•Developed and executed comprehensive unit tests and data validation queries for ETL processes in QA and pre-prod environments to validate data accuracy, completeness, and transformation logic adherence before promoting to production.•Worked in an agile environment, utilized Azure Devops tool to manage and track project tasks, iterations, and priorities efficiently.

•Implemented end-to-end Data migration projects for migrating client’s data from On-Premise to Cloud by leveraging Azure Cloud services and Microsoft PowerBI.•Responsible for building scalable pipeline using ADLS Gen2, Azure Databricks, Python and Apache spark, reducing data processing time by 56% and improving overall system performance.•Orchestrating and scheduling using multi task jobs in Databricks and storing the artifacts in DBFS. •Pycharm was used to commit code in Azure code repository and Pycharm inspection was used to improve overall code structure and version control.•Worked on Creating pipelines dynamically that can be used in loading multiple tables using single pipeline and custom pipelines to load data from SQL Server, DB2 to ADLs and Azure Synapse Database (Internal and external tables) using Azure Data Factory.•Worked on creating Dynamic Linked Services (Single Linked Services for SQL Server) to be used for connecting to different Databases while passing Server and Database Names Dynamically.•Worked on creating pipelines using Copy, Delete, Lookup, For Each, Switch, IF Condition, Execute Pipelines.•Integrated Logic Apps into Azure Data Factory to send Email notification with audit details and the total runtime of the pipeline.•Incorporated T-SQL to develop Stored Procedures to capture audit information and other data for reporting purpose using SQL Server Management Studio (SSMS).•Responsible for testing Data completeness, consistency, accuracy and validation post development and before production.•Used Jira and Azure Devops tool to manage and track project tasks, iterations, and priorities efficiently.•Actively involved in providing Demos to the clients on weekly framework progress.•Achievement - Awarded Accenture Pinacle Award FY21 and Avande's Sparkling star Award FY21 for performance and contribution in the team.

Worked as a ML engineer, created quality supervised data,Semi-supervised data tagging using LDA models along with developing a foundation in machine learning with Python and various algorithms and models used. Automated tasks using Python, Jupyter Notebook. Worked on various assets and created demos which involved IBM Watson services like Watson Discovery and Watson Assistant. Responsible for developing Interactive Bots with a purpose of automating customer care service for five of the Accenture's external clients and internal teams.Green Field Training in Java, Python, SQL, Big Data Tools - Hadoop, Hive , Cassandra, Spark and Kafka