How would you approach synchronizing client data that arrives as Excel files and may not match your schema, ensuring data quality and ease of use?

To synchronize client data that arrives as Excel files and may not match your schema, you would first establish a data mapping document that aligns each column in the Excel sheet with a target column in your warehouse. Next, you would build an ingestion pipeline that pulls the files from a shared location (e.g., a network drive or S3 bucket) and loads them into a staging table. During the load, you would apply validation rules: check for required fields, data types, value ranges, and duplicate keys. Any rows that fail validation would be flagged and sent back to the client for correction. To make the process easier for the client, you could provide a template Excel file with data validation rules and conditional formatting, and supply a simple upload portal or a scheduled script that automatically pulls the files. You would also set up automated alerts for missing or malformed files, and maintain a log of all synchronization runs. Finally, you would schedule incremental loads so that only new or changed rows are processed, reducing load time and ensuring consistency.