The task involved designing an end-to-end data pipeline architecture based on a given scenario. I was provided with a detailed sheet outlining the scenario and was required to create the solution entirely on paper. The focus was on addressing critical data engineering principles, including scalability, security, data quality, handling duplicate columns, schema design, staging databases, efficiency, fault tolerance, monitoring, automation, data versioning, compliance with regulatory standards, and robust data governance. Additionally, I had to account for edge cases such as schema evolution, managing high data velocity and volume spikes, and minimising downtime to ensure seamless operations. The final step involved presenting the architecture to a panel, answering their follow-up questions, and justifying my design decisions to demonstrate how the proposed solution met the outlined requirements and addressed potential challenges.