Andrew Dubynin

Owned and managed full-stack data infrastructure across Azure and Snowflake, supporting remote server environments. Deployed AI agents to replace manual workflows for account managers and Power BI developers and migrated pipelines to a new join key architecture.

Refactored retail point-of-sale data pipelines in Python and Snowflake, reducing runtimes by 80% and cloud computing costs by 60%. Improved product webscraping accuracy by 30% using outlier detection, data inference, and aggregation, and built reporting frameworks to detect data discrepancies.

Led corporate development initiatives for data frameworks, data warehouses, and data pipelines to improve efficiency and scalability. Built a Snowflake data governance system for credit risk model development, performed match key analysis with Python and Spark for identity resolution, administered sensitive-data server operations, and mentored senior developers on SDLC.

Designed SQL framework databases with normalization and ACID compliance, and handled DBA responsibilities including query optimization, server load analysis, and capacity planning. Integrated client data workflows using AWS, Azure, Airflow, and Snowflake, and built data governance for classification, lineage tracking, and compliance.

Designed and implemented solutions to create a fully automated data lifecycle and refactored legacy components to reduce runtime and code redundancy. Developed SSRS reports, procedures, functions, triggers, views, and SQL Agent jobs, and led a 30-day SQL training program for junior developers with contributions to the training wiki.

Processed and onboarded datasets and automated manual processes to support credit data analysis. Contributed SQL-based data preparation and analysis to improve downstream reporting and modeling readiness.

Focused on Mathematical Sciences and Mechanical Engineering at Virginia Commonwealth University.