PRATIK SHELAR

Project: Campaign Migration – IDFC First Bank

• Enhanced Data Processing & Campaign Optimization: Improved data workflows for better efficiency and scalability.
• Developed Spark Code: Built and optimized Spark applications using Scala, PySpark, Spark SQL, and DataFrames on AWS EMR.
• Led Migration from SAS to AWS PySpark: Successfully transitioned campaign processes, ensuring seamless execution and scalability.
• Performance Optimization (60-80% Improvement): Reduced execution time by fine-tuning Spark jobs and leveraging cloud-based processing.
• Data Aggregation Efficiency: Optimized ETL pipelines, improving data processing and storage strategies on AWS.

Project 1: Data Process Optimization – Axis Bank Bharat Bank
Optimized Data Processes: Improved data transfer efficiency for Agri, MSME, and rural markets.
SQL Development: Created 50+ complex SQL queries to support business analytics and reporting.
Streamlined Analytics: Designed parametric procedures, reducing processing time.
Performance Tuning: Resolved data issues, significantly improving query execution time.
Enhanced Cross-Selling: Leveraged DataMart to improve product targeting and sales strategies.

Project 2: Centralized Star Schema Data Mart
Data Mart Consolidation: Merged 4 legacy Data Marts into a centralized structure using Kimball's Star Schema in Azure Data Factory.
Optimized Data Integration: Integrated 5 dimension tables and 3 fact tables for seamless data processing.
Performance Improvement: Reduced processing time through efficient ETL design and automation.
Enhanced Query Performance: Implemented indexing and partitioning, improving data retrieval speeds.
Business Impact: Enabled faster and more efficient reporting, empowering analytics and data science teams.

Project: Enterprise Data Warehouse Development – Equity Insurance Ltd

• Designed & Developed EDW for financial, management, and rejection reports.
• Optimized SQL & ETL: Created 100+ SQL queries and 50+ Informatica mappings, improving data processing efficiency.
• Performance Tuning: Reduced job runtime by 40% through query optimization and transformation improvements.
• Advanced Data Transformations: Implemented aggregators, joiners, and sorters for efficient data flow.
• Issue Resolution & Stability: Resolved 90% of data issues and ensured ETL pipeline integrity.