Another scenario I normally encounter as a data modeler: data is made available in the Gold layer and BI reports consume it, but the BI team reports that queries on the Gold layer are taking too long and they cannot get reports rendered on time. How would you handle this situation?

First, I would profile the slow queries to identify bottlenecks: check execution plans, look for full scans, expensive joins, or missing statistics. Next, I would examine the Gold layer schema—ensure tables are partitioned on high‑cardinality, query‑friendly columns and that clustering keys are appropriate. If the data volume is large, consider using Delta Lake’s Z‑order or data skipping to reduce I/O. I would also evaluate the compute resources: increase the cluster size or use a higher‑performance instance family, and enable caching for frequently accessed tables. If the queries involve complex aggregations, I might pre‑aggregate data into materialized views or summary tables. Finally, I would set up monitoring dashboards (e.g., Databricks SQL Analytics) to track query latency and alert on thresholds, so we can proactively address performance regressions. Throughout, I would communicate with the BI team to understand their typical query patterns and adjust the schema or indexes accordingly.