1. Access Jira API: Use Jira’s REST API to extract the data you need. You will need to authenticate your request with your Jira username and API token. For example, to get issues from a project:
curl -u username:token -X GET -H "Content-Type: application/json" 'https://yourdomain.atlassian.net/rest/api/3/search?jql=project=YOURPROJECTKEY'‍
2. Pagination Handling: If you have a lot of issues, you will need to handle pagination to retrieve all data. Loop through the pages and collect the data until there is no more data to fetch.
3. Data Extraction: Extract the data in JSON format and save it to a file. For example, you might save the output to jira_issues.json.

1. Convert JSON to CSV: BigQuery can ingest data in various formats, but CSV is one of the simplest and most common. Use a script (Python, for example) to parse the JSON file and convert it to CSV format. Include only the fields you need.
import json
import csv

# Load the JSON data
with open('jira_issues.json') as file:
   data = json.load(file)

# Open a CSV file for writing
with open('jira_issues.csv', 'w', newline='') as file:
   csv_writer = csv.writer(file)
   
   # Write headers based on your Jira data structure
   headers = ['id', 'key', 'summary', 'status', 'assignee']
   csv_writer.writerow(headers)
   
   # Write the issues to the CSV file
   for issue in data['issues']:
       row = [
           issue['id'],
           issue['key'],
           issue['fields']['summary'],
           issue['fields']['status']['name'],
           issue['fields']['assignee']['displayName'] if issue['fields']['assignee'] else None
       ]
       csv_writer.writerow(row)‍
2. Ensure Data Integrity: Make sure your data types are consistent and match the schema you will define in BigQuery.

1. Create a Storage Bucket: In the Google Cloud Console, create a new Cloud Storage bucket if you don’t have one already.
2. Upload the CSV File: Use the gsutil command-line tool to upload your CSV file to the bucket.

gsutil cp jira_issues.csv gs://your-bucket-name/

1. Create a Dataset: In the BigQuery Console, create a new dataset where your Jira data will reside.
2. Define a Table Schema: Create a table with a schema that matches the CSV file structure. You can do this through the BigQuery web UI or using the bq command-line tool.

1. Create a Load Job: In the BigQuery Console, create a new load job to import the CSV data from your Cloud Storage bucket into the table you created. Specify the data format and the schema.
2. Monitor the Load Job: After starting the load job, monitor its progress. Once the job completes, verify that the data has been loaded correctly.

Run Queries: Now that your data is in BigQuery, you can run SQL queries against your dataset to analyze your Jira data.

Automation: To keep your BigQuery dataset up to date, you may want to automate this process. You can do this by creating a script or using Google Cloud Functions to periodically extract data from Jira, transform it, and load it into BigQuery.

Remember to handle any errors that might occur during the data extraction, transformation, or loading processes. Also, consider the costs associated with BigQuery storage and queries, and Google Cloud Storage.