是的，Airflow XCOM能够在BigQueryInsertJobOperator和BigQueryOperator中使用。以下是如何在Airflow DAG中使用XCOM从上一个任务中拉取数据并将其推送到下一个任务的示例：

from airflow import DAG
from airflow.contrib.operators.bigquery_operator import BigQueryOperator
from airflow.contrib.operators.bigquery_operator import BigQueryInsertJobOperator
from airflow.operators.python_operator import PythonOperator
from datetime import datetime, timedelta

default_args = {
    'owner': 'airflow',
    'depends_on_past': False,
    'start_date': datetime(2021, 1, 1),
    'email_on_failure': False,
    'email_on_retry': False,
    'retries': 1,
    'retry_delay': timedelta(minutes=5),
}

dag = DAG(
    'example_dag',
    default_args=default_args,
    description='A simple DAG',
    schedule_interval=timedelta(days=1),
)

def extract_data():
    data = [
        {"name": "Alice", "age": 25},
        {"name": "Bob", "age": 30},
        {"name": "Charlie", "age": 35}
    ]
    return data


def load_data(**kwargs):
    ti = kwargs['ti']
    data = ti.xcom_pull(task_ids='extract_data_task')
    sql = """
        INSERT INTO `myproject.mydataset.mytable` (name, age)
        VALUES ('{}', {});
    """.format(data['name'], data['age'])
    insert_task = BigQueryInsertJobOperator(
        task_id='insert_data_task',
        configuration={
            'query': {
                'query': sql
            }
        },
        dag=dag
    )
    insert_task.execute(context=kwargs)


extract_task = PythonOperator(
    task_id='extract_data_task',
    python_callable=extract_data,
    dag=dag
)

load_task = PythonOperator(
    task_id='load_data_task',
    python_callable=load_data,
    provide_context=True,
    dag=dag
)

extract_task >> load_task

在这个示例中，我们定义了两个任务：extract_data_task