<p>1. Abstract<br /> How do you design a data platform that can preserve years of history, support model development and stress testing, and still remain maintainable over time? </p> <p>In this hands-on workshop, participants will work through the architecture and engineering challenges behind building a robust data warehouse for Risk and Finance use cases. Together, we will explore data modelling in BigQuery, ingestion of structured and unstructured data, AI-assisted extraction from PDFs, pipeline engineering and the design of a model development platform. </p> <p>The session will combine discussion, problem-solving and practical work in GCP.</p> <p>2. Agenda<br /> Intro: Workshop objective, problem definition and requirements<br /> Session 1: Architecture – constraints introduced by Big Query. Efficient data model design.<br /> Session 2: Data ingestion – structured and unstructured data sources.<br /> Session 3: Using AI to extract structured data from PDFs<br /> Session 4: Pipeline Engineering<br /> Session 5: Model Development Platform</p> <p>3. Objectives<br /> Design a platform that continuously ingests data from Risk and Finance systems, preserves history indefinitely, supports model development, stress testing and analytical replay, and remains maintainable for decades. </p> <p>A practical session combining discussion and design work – building a robust data warehouse for model development purposes.</p> <p>4. Target audience and Prerequisites<br /> Software developers, data engineers and other technical professionals interested in data warehousing and platform design.  </p> <p>5. Technical requirements<br /> GCP account <br /> Personal laptop</p>