Zachary Pringnitz

Architected and optimized enterprise-scale data pipelines using Azure Data Factory, Databricks (PySpark), and Delta Lake, reducing data processing latency by 40% and supporting real-time analytics at scale. Led end-to-end development of a cloud-native data platform using Azure Synapse, Data Lake Gen2, and Snowflake, centralizing siloed datasets and enabling advanced BI reporting for executive stak

Designed and deployed end-to-end data pipelines using Azure Data Factory, Databricks (PySpark), and Snowflake, streamlining multi-source ingestion and reducing ETL execution time by 45%. Built real-time streaming solutions with Apache Kafka, Flink, and Spark Streaming, enabling near-instant analytics on customer behavior and fraud detection across the enterprise.

Developed and deployed AI-powered capabilities across End-to-End (E2E) Delivery, Promise, Sourcing, Transportation, and Inventory product portfolios, directly impacting millions of Walmart customers. Designed and implemented scalable Big Data solutions using Hadoop, Spark, and PySpark, enabling the processing of 100+ TB of e-commerce data for real-time insights and decision-making.

Analyzed methane emission data collected by SOOFIE sensors, leveraging Python (Pandas, NumPy) for data manipulation and Matplotlib/Seaborn for visualization to identify patterns and anomalies. Developed predictive models to estimate methane emissions using Scikit-learn and XGBoost, incorporating environmental and sensor data to enhance model performance.

Obtained a Master of Science in Data Science. The curriculum focused on advanced data analysis, machine learning, and statistical modeling techniques.

Completed a Bachelor of Science in Computer Science. The program provided a strong foundation in programming, algorithms, and software development principles.