Transformed financial data into actionable insights by leveraging ETL processes and Python programming within AWS Lambda functions, extracting information from diverse sources stored in AWS S3 Buckets. Collaborated on the design and maintenance of ETL pipelines, ensuring data consistency and compliance with standards, and loading it into S3 and Snowflake data warehouse.Engaged in data integration efforts, liaising with stakeholders to comprehend data sources, structures, and integration needs. Conducted data transformation and cleansing operations, upholding accuracy and compliance before loading into repositories. Optimized ETL workflows, identifying and resolving bottlenecks for enhanced data pipeline efficiency.Demonstrated commitment to data governance, adhering to policies and maintaining data security and privacy standards throughout ETL processes. Implemented error handling mechanisms and monitoring solutions, proactively resolving data pipeline issues.Generated comprehensive technical documentation, including data mappings, ETL process flow diagrams, and system configurations. Collaborated seamlessly with data analysts, business intelligence teams, and stakeholders to meet data needs and deliver solutions aligning with business objectives.Kept abreast of ETL trends, technologies, and best practices, driving continuous improvement in ETL processes and data pipeline reliability. Conducted thorough testing, data validation, and quality assurance checks to ensure and maintain data integrity.Proficient in utilizing SQL, AWS Lambda, AWS S3, AWS Glue, ETL, Python, PowerBI Reports, Github, stored procedures, and SAP BODS.

Build and publish data-pipelines from multiple data sources to form a unified master data system using Informatica; which provides 20% improved data quality. Develop ETL scripts to build a Data Virtualization layer which helps gather data using SQL stored procedures web services/API’s to make data accessible for all teams reducing data gaps by 15%.Collaborate with cross-functional teams (EM/PM) to translate business requirements, associated epics, and acceptance criteria for technological implementation and develop effective relationships to influence leaders. Deliver insightful and actionable recommendations that helped improve sales pipeline by 13%. Design Database Object Models by formulating data mapping documents for the application.Migrated data onto AWS Redshift and Salesforce reducing infrastructure costs by 30%. Designed API services that serve 100K API requests and 1+ million queries per day that help extract hundreds of terabytes of interaction data daily.Enable the cross-functional teams with a data-onboarding process that serves multiple business use-cases.

Implemented an efficient process to migrate 1600 tables with millions of records in real time from Oracle Database to MS SQL server using SSIS/Python Oracle Connect.Designed Data models leveraging “Entity-Relationship” concept, and “Dimensional Modelling (Star Schema and Snowflake Schema) that improved the database queries performance by 40%.Formulated Extract, Transform, and Load (ETL) processes, including document parsing techniques and managing large data sets, such as multi-TB-scale deployed environments and providing quarterly and weekly reports while adhering to 99.9% service-level agreements.Achieved Data Manipulation and retrieval to create insight-rich reports and extract data in SAS Visual Analytics to identify fraudulent customer behaviour with 80% efficiency.

Data Science Graduate Research Assistant under Dr. Joseph Gilkey (Program Director of Data Science) designed Text Analysis of yelp reviews by implementing NLP techniques to integrate the topic modeling with sentimental analysis using Machine Learning Algorithms to deep dive into business patterns. Received “Best Paper Award” from Academy of Business Research in Spring 2020 conference at New Orleans LA, USA for ‘Enhancing Customer Satisfaction: Based on topic modeling on yelp reviews’ Research paper published on image recognition of Galápagos Snake Species Using Deep Learning - using libraries to train models for image recognition. Successfully implemented four region-based convolutional neural network (R-CNN) architectures (models for image classification): Inception V2, ResNet, MobileNet, and VGG16 to perform image recognition and achieve overall accuracy of 95%.

Created custom stored procedures, functions as per business logic to extract the data from database.Assessed customers segments, those are eligible for loan by performing logistic and Decision trees.Reduced customer defaulter fraud rate from 15 % to 9 % by analyzing complex data sets.Applied Forecast methods to observe the sales trend and perform analysis by using python scripts.

Worked on Requirement, Data, and Gap Analysis of various source systems coming from multi systems. Enabled Business Process mapping for emerging requirements.Orchestrated models for calculating cost and capacity of the teams in an organization.Designed reports in Access, Excel using advanced functions not limited to vlookup, pivot tables, formulas that improved business decision by 13%.Automated iteration testing via Katalon Studio to validate data which reduced manual intervention by 50%.

Designed highly scalable database solutions that auto-scales based on growing business requirements.Developed iteration testing script to perform regression testing for the data flowed into the system which improved data quality by 25%.