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Here are some steps to follow for optimal data extraction from BigQuery and loading it into SQL Server:

1. Choose the appropriate method for data extraction: There are two commonly used methods for extracting data from BigQuery - using the web UI or using the command-line tool. The web UI is suitable for small datasets, whereas the command-line tool is better for large datasets as it supports parallelism and is faster.

2. Optimize your queries: Write optimized queries that use filters, aggregations, and other techniques to minimize the amount of data being extracted. This not only reduces the time taken for extraction but also reduces the cost of the operation.

3. Use a batch job: Use a batch job to extract data in chunks rather than extracting it all at once. This allows you to avoid timeouts and achieve better performance.

4. Choose the right format: Choose a format that is easy to load into SQL Server, such as CSV or JSON. Avro or Parquet formats are also good options as they are highly compressed and support schema evolution.

5. Use a tool for loading data: Use a tool such as SSIS (SQL Server Integration Services) or PowerShell scripts to automate the process of loading data into SQL Server. This not only saves time but also reduces the chances of errors.

6. Use the appropriate indexes: Create indexes on the SQL Server tables to optimize queries and increase performance. Make sure to test the queries and fine-tune the indexes to achieve optimal performance.

7. Monitor performance: Monitor the performance of the data extraction and loading process to identify bottlenecks and optimize accordingly. Ensure that the job completes successfully and that there are no data inconsistencies or errors.

By following these steps, you can effectively extract data from BigQuery and load it into SQL Server in an optimal and efficient manner.