Begin by accessing the Iterable API documentation on their official website. This will provide you with the necessary endpoints and authentication details required to extract data. Ensure you have the proper API credentials, such as an API key, for authentication purposes.

Use the Iterable API to extract the data you need. You'll likely use the REST API to make HTTP GET requests to the appropriate endpoints. For instance, if you want to extract user data, utilize the `/api/users` endpoint. Make sure to handle pagination if your data set is large, by using parameters like `page` or `limit` as specified in the API documentation.

Once the data is fetched, transform it into a CSV format. This can be done using a programming language like Python. After fetching the data via HTTP requests, parse the JSON response and write the data into a CSV file. Libraries such as `pandas` can simplify this process by allowing you to convert JSON directly into a DataFrame and then export it to a CSV file.

Log into your Snowflake account and navigate to the database where you want to import the data. Ensure you have created the necessary table schema that matches the structure of the CSV file. For instance, if your CSV file has columns for `user_id`, `email`, and `signup_date`, ensure your Snowflake table has corresponding columns.

Use the Snowflake Web Interface or SnowSQL (Snowflake's command-line client) to upload your CSV file to a staging area in your Snowflake environment. Create a named stage or use a user stage to store the file temporarily. You can use the `PUT` command in SnowSQL to accomplish this:
```
PUT file://path_to_your_file/your_file.csv @your_stage;
```

Execute a `COPY INTO` command in Snowflake to load the data from the stage into your table. Specify the file format options, such as `FIELD_OPTIONALLY_ENCLOSED_BY` if your data might contain commas. Example command:
```
COPY INTO your_table
FROM @your_stage/your_file.csv
FILE_FORMAT = (TYPE = 'CSV' FIELD_OPTIONALLY_ENCLOSED_BY = '"');
```

After loading the data, perform a simple query to validate that the data has been imported correctly. Check for the number of rows, data types, and any potential errors. You can run a query like:
```
SELECT FROM your_table LIMIT 10;
```
Compare the results with your source data to ensure accuracy and completeness. Adjust any discrepancies by revisiting the earlier steps if necessary.

By following these steps, you can efficiently transfer data from Iterable to Snowflake without relying on third-party connectors or integrations.