Uzma A. Email and Phone Number

• Designed scalable data models to integrate AI-powered solutions for call-center optimization.• Led client discovery sessions, reducing onboarding time by 30% by aligning technical designs with business needs.• Implemented data governance and monitoring tools, reducing discrepancies by 30% and ensuring high data integrity.• Utilized Python and Jupyter Notebook to perform data analysis, improving accessibility and reducing manual workload by 25%.• Created interactive dashboards using Power BI, improving stakeholder decision-making speed by 40%.

• Led the data migration from on-premises Teradata to AWS S3, ensuring seamless data transfer with zero data loss. Improved operational efficiency by 30% and reduced infrastructure costs by 25%, completing the project two weeks ahead of schedule.• Developed, optimized, and orchestrated ETL workflows using Teradata ETL tools, reducing runtime from 7 hours to 4 hours and automating tasks to reduce manual intervention by 50%. This resulted in a 50% improvement in SLA compliance and enhanced overall data processing efficiency.• Integrated Hadoop with Teradata, designing ETL processes to facilitate seamless data flow between platforms and supporting large-scale data integration and analysis of multi-terabyte datasets, which improved cross-platform data accessibility by 40%.• Wrote and optimized complex SQL queries, created stored procedures, and developed views to streamline data retrieval processes. This reduced query execution time by 30% and improved report generation times by 25%, increasing data accessibility for business users by 40%.• Maintained data quality across ETL processes by implementing proactive measures to identify and resolve data discrepancies. Reduced data errors by 40%, ensuring 99.5% data reliability for critical business intelligence reports.• Mentored a junior team member in SQL development and ETL best practices, contributing to a 20% increase in team productivity and fostering a culture of continuous learning.• Utilized Teradata Viewpoint for advanced query tuning, reducing resource consumption by 25% while handling peak loads of over 1,000 concurrent users.• Utilized Python libraries (Pandas, NumPy, Scikit-learn) to perform data analysis and develop machine learning models, reducing manual data processing by 40% and enabling predictive analytics for enhanced business insights.• Maintained comprehensive documentation of ETL processes and system architecture, reducing troubleshooting time by 35% and improving team onboarding efficiency.