Summary
Andres Barragan is a data engineer and data scientist with 8 years of software experience and 4+ years focused on cloud-native data platforms and production ML, primarily on Azure. He designs and implements secure, scalable ETL and real-time pipelines (Databricks, Synapse, ADLS Gen2, Event Hub) and has applied Medallion architecture and RBAC-backed deployments to deliver analytics and Power BI dashboards that drive business insights. Comfortable across Spark, PySpark, KQL, SQL, and Python, he builds production ML workflows from feature engineering through model deployment using tools from Scikit-learn and XGBoost to TensorFlow and Hugging Face. Past roles include containerizing legacy systems with Kubernetes, automating CI/CD, and instrumenting observability stacks (Prometheus/Grafana), reflecting a strong platform and DevOps sensibility beyond pure data work. Based in Tucson and academically grounded in CS from the University of Arizona, he brings a practical research background in neural signal and visual intelligence projects that informs his approach to applied ML.
9 years of coding experience
4 years of employment as a software developer
The University of Arizona