Airflow DAG skeleton writer

Write an Airflow 2.x DAG in Python that:

- Runs daily at 06:00 UTC
- Extracts data from a Postgres source
- Transforms it with a PythonOperator calling pandas
- Loads the result into BigQuery
- Sends a Slack notification on failure
- Uses catchup=False and max_active_runs=1

from datetime import datetime, timedelta
from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.providers.slack.operators.slack import SlackAPIPostOperator

default_args = {
    "owner": "meridian",
    "retries": 2,
    "retry_delay": timedelta(minutes=5),
    "on_failure_callback": None,
}

def extract(**ctx):
    pass

def transform(**ctx):
    pass

def load(**ctx):
    pass

with DAG(
    dag_id="etl_postgres_to_bigquery",
    default_args=default_args,
    schedule="0 6 * * *",
    start_date=datetime(2025, 1, 1),
    catchup=False,
    max_active_runs=1,
    tags=["meridian", "etl"],
) as dag:

    extract_task = PythonOperator(task_id="extract", python_callable=extract)
    transform_task = PythonOperator(task_id="transform", python_callable=transform)
    load_task = PythonOperator(task_id="load", python_callable=load)

    extract_task >> transform_task >> load_task