Suppose you need to ingest data from an API service into a data lake, build a medallion architecture data bridge, and then make it available for API consumption in tabular form. How would you approach this typical data ingestion and data modeling exercise?

First, I define the API contract and schedule a data extraction job (e.g., using Airflow or Azure Data Factory). Raw JSON responses are stored in a bronze layer of the data lake. Next, I create a silver layer where I parse the JSON, flatten nested structures, and apply data quality rules (null checks, type casting). I then build a medallion architecture: bronze (raw), silver (cleaned), gold (aggregated). The data bridge is implemented with dbt models that join silver tables into a unified schema suitable for analytics. Finally, I expose the gold tables through a REST API (e.g., using FastAPI) or a BI connector, ensuring the output is tabular and paginated. Throughout, I maintain lineage, versioning, and automated tests to guarantee consistency and reliability.