Lakehouse Solution Pitch

Pitch the lakehouse solution to a panel of 5, walking through a data solution you have designed and implemented end to end and defending your decisions.

Asked in the Panel Presentation stage. Panel presentation round to 5 interviewers.

Your presentation should be grounded in a real solution you personally designed and implemented. Explain the problem, data flow, architecture, technology choices, operational behavior, and measurable outcomes without relying on unexplained diagrams or product terminology.

1. Define the problem, users, requirements, and success criteria.
2. Walk through ingestion, storage, transformation, governance, and serving.
3. Explain batch and streaming decisions, data quality controls, and failure recovery.
4. Describe any ML lifecycle, feature engineering, training, serving, and feedback loop.
5. Defend alternatives, tradeoffs, cost, security, monitoring, and operational ownership.

Use Databricks terminology where relevant, such as Delta Lake, Unity Catalog, Auto Loader, Structured Streaming, Workflows, MLflow, Feature Engineering in Unity Catalog, and Model Serving. Be prepared for follow-up questions from all five interviewers.