Karan Milan Thevar

<p>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.<br>
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.</p>

• 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.

<p>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.<br>
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.<br>
Developed SQL Agent jobs to automate extraction, transformation, and loading processes, leveraging the Azure Data Factory for efficient pipeline deployment.<br>
Designed PowerShell scripts to streamline data scrubbing, reducing erroneous data by 70%, and implemented Snowflake stored procedures for incremental data loads.<br>
Created and enhanced Common Data Models (CDM) using DBT & Snowflake, achieving a 40% runtime efficiency improvement.<br>
Partnered with downstream teams to resolve P1 issues, ensuring uninterrupted data system stability and less than 5% downtime.<br>
Utilized Confluence for documenting processes and actively contributed to Scrum/Kanban workflows for iterative project delivery.</p>

<p>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.<br>
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.<br>
Developed SQL Agent jobs to automate extraction, transformation, and loading processes, leveraging the Azure Data Factory for efficient pipeline deployment.<br>
Designed PowerShell scripts to streamline data scrubbing, reducing erroneous data by 70%, and implemented Snowflake stored procedures for incremental data loads.<br>
Created and enhanced Common Data Models (CDM) using DBT & Snowflake, achieving a 40% runtime efficiency improvement.<br>
Partnered with downstream teams to resolve P1 issues, ensuring uninterrupted data system stability and less than 5% downtime.<br>
Utilized Confluence for documenting processes and actively contributed to Scrum/Kanban workflows for iterative project delivery.</p>

<p>Generated and modified stored procedures in SSMS & Oracle to design dashboards and reports using SSRS.<br>
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%.<br>
Develop and enforce data models to ensure accuracy, consistency, and reliability across all systems.<br>
Build and optimize ETL workflows to seamlessly extract, transform, and load data from multiple sources into the central data warehouse using SSIS.</p>

• 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.