Koundinya Venkata Upadhyayula Uvs

About Koundinya Venkata Upadhyayula Uvs

Data Analyst with 6+ years of experience delivering enterprise-scale analytics solutions across finance and healthcare domains.Certified in:• Microsoft Power BI Data Analyst Associate (PL-300)• Microsoft Fabric Data Engineer Associate (DP-700)Both from MicrosoftThis combination enables me to design end-to-end analytics ecosystems — from ingestion and transformation to semantic modeling and executive dashboards.Core Strengths:• Advanced SQL & performance tuning• Enterprise Power BI architecture & DAX optimization• Data modeling (Star Schema, Medallion Architecture)• Snowflake & Azure Data Factory pipelines• Predictive analytics (ARIMA, XGBoost, Random Forest)• Governance, RBAC, and data complianceImpact Delivered: Reduced ETL runtime by 40% using PySpark Improved SLA compliance by 32% Reduced healthcare claim denials by 20% Built executive-level BI dashboards for financial forecastingI specialize in bridging business intelligence and modern data platforms — ensuring stakeholders receive reliable, scalable, high-performance analytics.Open to Senior Data Analyst / BI roles focused on enterprise modernization and advanced analytics.

Experience

1. Data Analyst

   Wells Fargo

   Nov 2024 — Present · Dallas, TX, US

   Led design, implementation, and strategic direction of a real-time fraud analytics solution, integrating streaming data,predictive modeling, and cloud-native analytics to mitigate anomalous financial activity efficiently.• Engineered low-latency ingestion pipelines using Apache Kafka, processing over 1 million daily transactions, reducinglatency by 18%, while establishing scalable, reliable standards across multiple operational teams.• Built, validated, and deployed fraud classification and anomaly detection models using Python, SQL, and Scikit-learn,improving accuracy by 15%, reducing false positives, and mentoring junior analysts effectively.• Designed and automated interactive dashboards using Power BI with advanced DAX KPIs, delivering real-time fraudinsights to 3 core risk management teams, guiding operational decisions.• Conducted statistical analysis using ANOVA, logistic regression, and A/B testing on datasets containing millions oftransactional records, validating models, identifying fraud drivers, and supporting evidence -based mitigation policies.• Optimized large-scale analytical workloads in AWS Redshift using SQL tuning, partitioning, and indexing, reducing queryexecution time by 40% and enabling near real-time fraud detection across high-volume transactional datasets.• Architected secure, scalable cloud data pipelines, leveraging AWS S3 for encrypted storage, Glue for automated ETL andschema management, and Lambda for event-driven fraud scoring workflows across 1+ TB of transactional data.• Partnered with Agile cross-functional teams, including engineers, risk analysts, compliance stakeholders, & product managers,improving sprint delivery, enforcing governance, & ensuring seamless organization-wide rollout of analytics solutions.

Education

• The Regency Public School

  SSLC, High School

  2010 — 2011

• Webster University

  Master of Arts - MA, Information Technology

• Advanced Training Institute, Hyderabad

  High School Diploma, Electronic Mechanic

• St.Mary's group of Institutions

  Diploma, Electronics and Communication Engineering Technology/Technician

• Vignana Bharathi Institute of Technology

  Bachelor of Technology - BTech, Information Technology

  2019 — 2022