Optimized ETL pipelines using Azure Data Factory, Databricks & Snowflake, improving data accuracy and centralizing reporting for high-performance environments.Designed and implemented advanced data integration solutions, boosting operational efficiency with a 40% improvement in data retrieval speeds.Migrated legacy MapReduce applications to PySpark, improving performance by 30% and simplifying business logic implementation.Developed Python MapReduce programs to process data and store it efficiently in Hive tables.Led the creation of automated workflows using Power Automate and Power Apps, reducing manual processes by 40% and enhancing operational speed.Architected Power BI dashboards with row-level security and DAX, delivering tailored insights while ensuring compliance with internal standards.Established DevOps governance protocols, reducing report update cycles by 25% and improving version control efficiency.Enhanced database performance (SQL Server & Snowflake), cutting query times by 40% through optimized indexing and table structures.Developed and deployed AI/ML models using TensorFlow, PyTorch, and Scikit-learn to enhance data pipelines and enable data-driven decisions that automated decision-making, reducing manual intervention by 30%.Managed historical data loads to Cloud Storage using Hadoop utilities and loaded data into Big Query using BQ tools.Designed and developed Kafka producers, consumers, and brokers for stream processing using Java.Worked with Google Cloud Functions in Python to load data into Big Query from CSV files in GCS.Developed an ASP.NET MVC web interface to monitor production KPIs, improving scalability, security, and real-time data integration.Implemented data quality frameworks and CI/CD processes, ensuring data integrity and streamlining production releases.Collaborated with cross-functional teams, applying Agile and SDLC methodologies to foster efficient, problem-solving-driven development cycles.

Developed and managed ETL workflows using SSIS and Azure Data Factory, ensuring seamless data extraction, transformation, and loading for key business applications which reduced the downloading time 30%Developed a server-based Python ETL pipeline to replace a C# Azure solution, enhancing job scheduling with dependency management, error handling, and job chaining, increasing data processing speed by 50% and reducing infrastructure costs by 20%.Integrated Amazon Redshift for faster query performance in data warehousing apps, cutting down processing times by 25%.Automated data workflows with Airflow and Luigi, reducing manual intervention and speeding up data processing by 20%.Conducted UAT testing for system enhancements, ensuring business requirements were met and minimizing errors post-deployment.Replaced Anaconda-based Python distribution with Pip, optimizing deployment and package management, reducing download times by 30%.Built robust data cleaning and preprocessing pipelines using Pandas and NumPy, improving data quality and accuracy by 35% for downstream analytics.Integrated Python with SQL databases (SQLAlchemy), streamlining data extraction and preprocessing, reducing query response time by 40% and streamlining processes by 30%.Automated, configured, and deployed instances within the AWS environment and data centers, using EC2, CloudWatch, CloudFormation, and security groups, reducing infrastructure costs by 15%Developed an automated Python-based workflow for Adobe document generation, cutting document processing time by 60% and boosting team productivity by 20%.Enhanced consistency and maintainability of proprietary Python code by standardizing argparser and logger package usage, reducing code complexity by 30% and improving debugging efficiency by 25%.Implemented a user-friendly, API-accessible report generator for Shiplt, integrating with its underlying VB.Net code, decreasing report generation time by 40%.

Utilized Python (Pandas, NumPy) to extract, manipulate, and prepare large-scale financial datasets, ensuring high-quality data for analysis and driving informed decision-making.Migrated on-premises Data Warehouses and Data Marts to AWS Cloud Data Warehouses, optimizing storage and access using Amazon Redshift, improving query performance by 30%.Leveraged Amazon RDS for MySQL databases, ensuring high availability, durability, and security of critical financial data in the cloud with 99.9% uptime.Developed and optimized SSIS packages for seamless data movement, transformations, improving workflow efficiency by 20%.Designed and developed interactive visualizations using Tableau and Matplotlib, presenting key trends to stakeholders and improving data-informed decision-making.Crafted complex DAX and T-SQL queries, applying advanced techniques like joins, subqueries, and indexing to optimize query performance and support reporting needs.Conducted A/B testing and User Acceptance Testing (UAT) on new data analytics tools, enhancing functionality and usability for end-users, increasing adoption by 15%.

• Developed 10+ web services and applications using Spring, Hibernate, and Core Java, improving system efficiency by 30% and reducing processing times.• Engineered components for processing XML messages, using JMS to put them into queues reducing message handling latency.• Created 50+ JUnit test cases for web services using HTTP Client to imitate web clients improving test coverage by 40.• Migrated 5+ existing applications into REST-based microservices to provide all the 100% CRUD capabilities using Spring Boot.• Leveraged Spring Boot to develop and deploy 8+ cloud microservices, reducing development time by 40% and streamlining configuration management.

Full Stack Web development along with Problem Solving.Technical Stack Learnt: Node JS, React JS, Express JS, Mongo DB, HTML, CSS, JavaScript, and BootstrapParticipated in various coding contest organized by the platformWorked on various projects like Quiz App, Bomberman Game kind of Minesweeper, Expense Tracker, IMDB App, Math clash Game, Notes App.