Begin by setting up a Google Cloud Platform project if you don’t already have one. Go to the Google Cloud Console, create a new project, and make a note of the Project ID. Ensure that billing is enabled for your project.

Navigate to the GCP Console's API & Services dashboard. Enable both the BigQuery API and the Cloud Storage API. This is essential as you will need these services to store and analyze your data.

Sign up or log in to the Exchange Rates API service. Generate an API key, which you will use to authenticate your requests to the API. Note down the endpoint URL and any query parameters you might need for fetching the data.

Write a Python script to perform an HTTP GET request to the Exchange Rates API. Use libraries such as `requests` to fetch the data. Ensure your script processes and formats the data correctly, typically in JSON or CSV format. The script might look like this:
```python
import requests
import json
api_key = 'YOUR_API_KEY'
url = f'https://api.exchangeratesapi.io/v1/latest?access_key={api_key}'
response = requests.get(url)
data = response.json()
# Process and save the data to a local file
with open('exchange_rates.json', 'w') as f:
json.dump(data, f)
```

Use the Google Cloud SDK to upload your JSON or CSV file to a Google Cloud Storage bucket. First, create a storage bucket via the GCP Console. Then, use the `gsutil` command-line tool to upload your file:
```bash
gsutil cp exchange_rates.json gs://your-bucket-name/
```

Access the BigQuery Console, and navigate to your dataset or create a new one. Use the BigQuery Data Transfer Service to load data from your Google Cloud Storage bucket into BigQuery. You can do this through the UI by selecting "Create Table" and specifying the source as your JSON or CSV file in Cloud Storage. Configure the schema appropriately to match the data structure.

To automate the data transfer process, schedule a cron job on your local machine or a VM instance in GCP to run your Python script at regular intervals. Ensure the script fetches the latest data, uploads it to Cloud Storage, and then loads it into BigQuery. Use `cron` for Linux or Task Scheduler for Windows to set up these periodic tasks, ensuring the entire pipeline runs smoothly and consistently.
By following these steps, you can efficiently move data from the Exchange Rates API to BigQuery without relying on third-party connectors.