Utilize Python scripting and libraries such as Pandas and NumPy to perform data manipulation, cleansing, and transformation tasks within ETL processes, enhancing flexibility and extensibility in data engineering workflows.Leverage DBT's testing framework to ensure the accuracy and reliability of data transformations, reducing the risk of errors in downstream analysis.Utilize advanced SQL techniques, including window functions and CTEs, to perform in-depth analysis and derive actionable insights from complex datasets.Using Snowflake to create Tables, views and automating Transformations accross layers Raw,Cleaned, Transformaed and modeled layers.Working on Snowflake modelling using data warehousing techniques, data cleansing, Slowly Changing Dimension phenomenon, surrogate key assignment and change data capture.Develop comprehensive ETL pipelines utilizing SQL, Snowflake, and DBT to efficiently ingest, transform, and load diverse data sets onto the Client Data Platform, ensuring data integrity and accuracy.Designing complex ETL mappings, and reusable transformations using Regular Expressions, Dynamic Lookups, and Update Strategies for slowly changing dimensions.Develop data ingestion workflows to read data from various sources and write it to Avro, Parquet, Sequence, JSON, and ORC file formats for efficient storage and retrieval.

• NLP/ML: Analyzed historical service desk tickets to identify common issues and patterns, reducing ticket resolution time by 40%.• Engineered features to categorize tickets based on the descriptions, improving ticket categorization accuracy by 35%.• Trained the model using various classification algorithms and fine-tuned it to achieve high accuracy in ticket categorization.• Developed a Pricing Optimization Model: Designed and implemented a machine learning model using Azure Auto ML to recommend optimal discount ranges (minimum and maximum) for new equipment pricing, enhancing sales strategies.• Real-time Deployment and Feature Selection: Deployed the model as a real-time REST API on Azure Kubernetes Service (AKS) and utilized feature importance techniques to select key features such as rise, speed, load, installation area, and sales category.• Achieved Significant Business Outcomes: Improved margins by 3% and increased revenue by 45% year-over-year in France, and enhanced booked units sold by 4.5% with a 23% revenue increase year-over-year in Germany, by providing sales reps with data-driven discount recommendations.

• Computer Vision: Face recognition model with Viola-Jones algorithm. Used Haar-cascade classifier to detect facial properties.• Trained the CNN model to predict the gender and age group of customers with an accuracy of 72% for age and 82% for gender.• Attained 90% accuracy in post covid travel trend prediction through a Python random forest model deployed as a Flask API• Worked for GameStop E-commerce website support, tested, debugged, and documented software programs. Monitored system performance using tools like New Relic and Datadog, reducing downtime by 20%.• Worked in an Agile environment, adapted quickly to changing requirements and priorities, contributing effectively to deliverables.• Implemented PayPal and Google Pay payment integrations for Global Pay on the Salesforce Commerce Cloud platform.• Proficient in utilizing AWS S3 for data retrieval, storage, and management, reducing data retrieval time by 30%.• Worked on Google Cloud Platform services for monitoring website traffic, analyzing user behavior, and optimizing site performance, leading to a 20% increase in user engagement.

Trained on salesforce Sales and commerce cloud and worked on Student examinations portal development