Karan Milan Thevar

Led a cross-functional Data Operations team of 4 developers and 2 Network Operations Center (NOC) members to optimize data management processes and enhance operational efficiency.
Designed and implemented a DataOps Studio platform, reducing client onboarding time from 6 weeks to 2 weeks and cutting company costs by 75% through optimized data workflows.

• Team Lead: Led a cross-functional Data Operations team of 4 developers and 2 Network Operations Center (NOC) members to optimize data management processes and enhance operational efficiency.
• Platform Architecture: Designed and implemented a DataOps Studio platform, reducing client onboarding time from 6 weeks to 2 weeks and cutting company costs by 75% through optimized data workflows

• Data Pipeline Optimization: Engineered automated ingestion pipelines handling over 100GB of weekly raw data across 40+ clients, embedding robust fail-recovery mechanisms for seamless operations using Azure Data Factory
• Lead Cloud Migration: Spearheaded migration of data from MS SQL to Snowflake, reducing processing time by 50% and enabling scalable data solutions for 40+ oncology care customers with more than 20 different data sources.
• ETL Automation: Developed SQL Agent jobs to automate extraction, transformation, and loading processes, leveraging the Azure Data Factory for efficient pipeline deployment.
• Data Integrity & Transformation: Designed PowerShell scripts to streamline data scrubbing, reducing erroneous data by 70%, and implemented Snowflake stored procedures for incremental data loads.
• Data Modeling: Created and enhanced Common Data Models (CDM) using DBT & Snowflake, achieving a 40% runtime efficiency improvement.
• Collaboration & Problem-Solving: Partnered with downstream teams to resolve P1 issues, ensuring uninterrupted data system stability and less than 5% downtime
• Documentation & Agile Practices: Utilized Confluence for documenting processes and actively contributed to Scrum/Kanban workflows for iterative project delivery.

Engineered automated ingestion pipelines handling over 100GB of weekly raw data across 40+ clients, embedding robust fail-recovery mechanisms for seamless operations using Azure Data Factory.
Spearheaded migration of data from MS SQL to Snowflake, reducing processing time by 50% and enabling scalable data solutions for 40+ oncology care customers with more than 20 different data sources.
Developed SQL Agent jobs to automate extraction, transformation, and loading processes, leveraging the Azure Data Factory for efficient pipeline deployment.
Designed PowerShell scripts to streamline data scrubbing, reducing erroneous data by 70%, and implemented Snowflake stored procedures for incremental data loads.
Created and enhanced Common Data Models (CDM) using DBT & Snowflake, achieving a 40% runtime efficiency improvement.
Partnered with downstream teams to resolve P1 issues, ensuring uninterrupted data system stability and less than 5% downtime.
Utilized Confluence for documenting processes and actively contributed to Scrum/Kanban workflows for iterative project delivery.

Engineered automated ingestion pipelines handling over 100GB of weekly raw data across 40+ clients, embedding robust fail-recovery mechanisms for seamless operations using Azure Data Factory.
Spearheaded migration of data from MS SQL to Snowflake, reducing processing time by 50% and enabling scalable data solutions for 40+ oncology care customers with more than 20 different data sources.
Developed SQL Agent jobs to automate extraction, transformation, and loading processes, leveraging the Azure Data Factory for efficient pipeline deployment.
Designed PowerShell scripts to streamline data scrubbing, reducing erroneous data by 70%, and implemented Snowflake stored procedures for incremental data loads.
Created and enhanced Common Data Models (CDM) using DBT & Snowflake, achieving a 40% runtime efficiency improvement.
Partnered with downstream teams to resolve P1 issues, ensuring uninterrupted data system stability and less than 5% downtime.
Utilized Confluence for documenting processes and actively contributed to Scrum/Kanban workflows for iterative project delivery.

Generated and modified stored procedures in SSMS & Oracle to design dashboards and reports using SSRS.
Delivered interactive dashboards to track key metrics like customer attrition rate and reasons for leaving, which helped in driving informed decision-making for stakeholders and reduced the attrition rate by 30%.
Develop and enforce data models to ensure accuracy, consistency, and reliability across all systems.
Build and optimize ETL workflows to seamlessly extract, transform, and load data from multiple sources into the central data warehouse using SSIS.

• Data Pipeline Optimization: Engineered automated ingestion pipelines handling over 100GB of weekly raw data across 40+ clients, embedding robust fail-recovery mechanisms for seamless operations using Azure Data Factory
• Lead Cloud Migration: Spearheaded migration of data from MS SQL to Snowflake, reducing processing time by 50% and enabling scalable data solutions for 40+ oncology care customers with more than 20 different data sources.
• ETL Automation: Developed SQL Agent jobs to automate extraction, transformation, and loading processes, leveraging the Azure Data Factory for efficient pipeline deployment.
• Data Integrity & Transformation: Designed PowerShell scripts to streamline data scrubbing, reducing erroneous data by 70%, and implemented Snowflake stored procedures for incremental data loads.
• Data Modeling: Created and enhanced Common Data Models (CDM) using DBT & Snowflake, achieving a 40% runtime efficiency improvement.
• Collaboration & Problem-Solving: Partnered with downstream teams to resolve P1 issues, ensuring uninterrupted data system stability and less than 5% downtime
• Documentation & Agile Practices: Utilized Confluence for documenting processes and actively contributed to Scrum/Kanban workflows for iterative project delivery.