David Felipe Vanegas Ramirez

Advanced Data Engineer @ Loka

About

David is a data engineer at Loka, a technology consulting firm, where he designs and maintains data platforms on AWS for clients across multiple industries. With nearly five years of experience in the field, his day-to-day work revolves around Apache Iceberg, PySpark, Airflow, and production lakehouse architectures, not in tutorials, but in real systems with real incidents. I hold three AWS certifications, including Solutions Architect Associate and a Databricks certification, reflecting both my command of cloud infrastructure and my hands-on experience with distributed data processing. I have led projects ranging from ClickHouse replication pipelines to Iceberg table maintenance jobs running on Kubernetes, and have recently explored the intersection of data engineering and AI agents, building personal tooling on top of the Anthropic SDK. Based in Bogotá, I believe there is a shortage of practical Spanish-language technical content on modern data engineering, and that PyCon Colombia is exactly the right place to start closing that gap.

Workshop

Artificial IntelligenceData ScienceCore Python

From S3 to AI Agent: Your First Queryable Lakehouse

FORMAT: WorkshopLEVEL: IntermediateLANGUAGE: Spanish

AI agents are only as good as the data they can query. The problem is that most agents built today are connected to outdated CSVs, unstructured databases, or simply nothing. What if your agent could query a real lakehouse — with versioning, schema evolution, and time travel — using natural language? In this workshop we will build exactly that, from scratch, using only open source tools that run on your laptop. What we will build together: Starting from a fully local stack based on Docker Compose, we will set up a functional lakehouse architecture using MinIO as S3-compatible storage, Apache Iceberg as the table format, Project Nessie as a Git-like versioned catalog, and Trino as the SQL query engine. On top of that, we will build an MCP server in Python that exposes our Iceberg tables as tools queryable by an AI agent — and we will finish by connecting Claude so it can query our lakehouse in natural language. What you will learn: How a modern lakehouse really works under the hood — without managed services hiding the magic How Apache Iceberg enables schema evolution, time travel, and row-level deletes on object storage How to build an MCP server in Python that turns SQL queries into tools for AI agents Why this open source architecture mirrors exactly what companies like Netflix, Airbnb, and modern data teams use in production Why open source? We deliberately replace AWS S3 with MinIO, AWS Athena with Trino, and AWS Glue with Project Nessie — not because AWS is bad, but because understanding the real components makes you a better engineer, and because this workshop should be accessible to everyone, regardless of whether you have an AWS account or not. At the end of the workshop you will have a functional lakehouse running on your machine, a working MCP server, and an architecture you can take directly to your next project.

David Felipe Vanegas Ramirez

Advanced Data Engineer @ Loka

View talk

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