From ETL to Agentic Workflows: The Evolution of Data Engineering in the Generative AI Era

FORMAT: WorkshopLEVEL: All levels LANGUAGE: Spanish

This hands-on workshop will explore how data engineering is evolving from traditional ETL-based pipelines toward intelligent agent-driven systems capable of reasoning, planning, and executing tasks autonomously. Through hands-on exercises, participants will learn the fundamental concepts behind agentic workflows, the new architectural patterns emerging in the industry, and the modern Python libraries that enable building these types of solutions. Tools for agent creation, task orchestration, integration with language models, and automation of complex processes will be covered. Upon completion, attendees will have built functional examples and will understand how to apply these new capabilities to transform traditional data processes into more dynamic, autonomous, and scalable systems.

Workshop requirements

Databricks Community, local Python.

Speaker

Jose Arturo Osorio Londoño

Senior Tech Lead @ Lovelytics

Hello, I am José Arturo Osorio, Senior Tech Lead at Lovelytics, where I lead the Data Science, MLOps, and Generative Artificial Intelligence team for Latin America. With more than 8 years of experience in the data science field, I have grown alongside the evolution of the industry, working across the full spectrum of machine learning and artificial intelligence technologies. My experience ranges from classical machine learning models, including classification and regression with tree-based algorithms, to statistical modeling such as time series analysis and survival models. I have also developed deep learning solutions for computer vision, especially in semantic segmentation and named entity recognition (NER). I have applied these technologies across diverse sectors, including banking, services, and healthcare, always with a direct focus on business needs and value generation for clients. From a technical perspective, I have designed and implemented cloud-native machine learning solutions using mainly Azure, AWS, and Databricks. I specialize in developing AI and machine learning systems that generate tangible business impact, with measurable results aligned with strategic objectives. I also hold a master's degree in areas related to analytics and artificial intelligence, as well as professional certifications in AWS, Databricks, and Microsoft Azure.

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