SAMARPIT TULI

• Automated ETL processes, reducing manual work by 80% and saving $15k yearly. Improved data accessibility and efficiency. Technology Used : MySQL, Snowflake, HEVO
  
• Developed PII/PHI monitoring on SNOWFLAKE DATAWAREHOUSE to ensure compliance with SOX regulations, resulting in 99% reduction in data breaches and improved trust with stakeholders.
  
• Led initiatives to optimise AWS Cloud costs, delivering a $30k monthly savings. Strengthened cost management practices and supported nancial objectives.
  
• Consolidated integration tools, saving approximately 33k yearly. Reduced tech debt and streamlined processes for increased efficiency.
  
• Led the seamless migration of 50 PAYCHECK DB tables to AWS DMS, MYSql Aurora, and Stored Procedures, leading to a 40% increase in data processing efficiency and enhanced system reliability.
  
• Supported business intelligence initiatives by constructing dimensional models optimized for reporting purposes on Snowflake Datawarehouse .

Project 1: Data Migration from On-Prem using ADF, ADB, Snowake, ADLS Gen2

• Worked with data analytics team to identify the business requirements and develop and design ETL ow and Architecture
  
• Developed data pipelines to ingest and process large datasets.
  
• Developed Spark applications using PySpark and Spark SQL in Databricks for data extraction, transformation, and aggregation from ADLS Gen2
  
• Monitored system health and performance metrics to ensure smooth functioning of the system
  
• Lead and coached 2-3 technical team member or data engineers in assigning and carrying out project (s)
  

Project 2 : Optimising Healthcare Claims Processing with Snowflake ETL

• Led the design and implementation of a Snowflake-based ETL pipeline to streamline healthcare claims processing, reducing manual effort by 40% and improving accuracy to 99.5%.
• Developed complex SQL transformations within Snowflake to cleanse, standardize, and aggregate claims data from disparate sources, ensuring compliance with industry regulations.
• Leveraged Snowflake's scalability to handle large volumes of claims data, enabling efficient processing and analysis.
• Collaborated with business stakeholders to gather requirements, translate them into technical specifications, and deliver a solution that met their needs.
• Monitored and optimized ETL pipeline performance, identifying bottlenecks and implementing improvements to ensure high availability and throughput.

Project 1: Data Migration from On-Prem using Azure Data FactoryTechnology used : ADF, MySQL , LogicApps, SQL

• Implemented data migration using Azure Data Factory using metadata framework following Medallion Architecture)
  

Project 2: BI dashboards migration from Google Data Studio to MicrosStrategyTechnology used : Snowflake, MySQL ,Microstrategy

• Designed and implemented dashboards using MicroStrategy
• Implemented Datawarehouse using Snowflake
  
• Created various data models for reporting purpose
• Reduced 90% of manual work

Project 3: Retails Analytics for Elevaate Customers: Azure Databricks

Technology used : ADF (Azure Data Factory), Azure Databricks

• Built applications using PySpark and Spark SQL to perform analytics efficiently on huge data sets using Azure Services like ADB, ADF
• Used spark transformations and actions to build simple/quick and complex ETL applications
• Involved in performance optimisation of Spark jobs and designed efficient queries to query data
• Developed detailed plans for achieving desired analytics deliverables and goals
  

• Transform, load data from various Sources to Azure Data Storage services using a combination of Azure Data Factory, PySpark, ADLS Gen2
• Reduced report generation time by 80% through automation using Google dashboards and SQL, resulting in increased productivity and cost savings.
• Build, monitor, and optimise ETL and ELT processes with data models on Snowflake Datawarehouse
• Migrate solutions from on-premises setup to cloud-based platform (Snowflake)
• Understand and implement the latest delivery approaches based on data architecture
  
• Project documentation and tracking based on understanding user requirements
  
• Perform data integration with third-party tools including architecting, designing, coding, and testing phases
  
• Manage documentation of data models, architecture, and maintenance processes
• Continually review and audit data models for enhancement
  
• Maintenance of ideal data pipeline based on ETL tools