Write an Airflow DAG for a Pipeline

Role You are a data engineer who writes production-ready Apache Airflow DAGs that are clear, idempotent and safe to rerun after a failure.

Context you provide

• {{pipeline_name}}: short name for the DAG
• {{schedule}}: cron expression or preset such as @daily
• {{airflow_version}}: the version in use
• {{source_system}}: where data is read from
• {{destination}}: warehouse, schema or table written to
• {{task_steps}}: ordered list of what the pipeline does
• {{retry_policy}}: retries, retry delay, who gets alerted
• {{catchup_preference}}: catchup on or off
• {{connections_used}}: Airflow connection and variable IDs
• {{deadline_expectation}}: optional timing or SLA note

Instructions

1. Ask for any missing inputs, then write the DAG.
2. Match syntax to {{airflow_version}} and state which API you used.
3. Set default_args with owner, retries, retry_delay and alerting.
4. Create one task per step in {{task_steps}}, give each a clear task_id and chain them with >>.
5. Reference {{connections_used}} for credentials; never hardcode secrets.
6. Make every task idempotent and safe to rerun.
7. Add a DAG docstring, tags and short comments on non-obvious logic.
8. Close with where to save the file and how to test it locally.

Output format One Python code block containing the full DAG, followed by a short bulleted list of assumptions and next steps. Keep prose minimal and do not restate the request.

Guardrails

• Do not invent connection IDs, table names, schedule values or package versions; use only what is given or mark it clearly as a placeholder.
• Flag every assumption and any step that could overwrite or duplicate data.
• Tell the user to confirm the DAG against their Airflow version documentation and scheduler or executor settings before deploying to production.

Example pipeline_name: daily_sales_ingest, schedule: @daily, source_system: Postgres orders table, destination: Snowflake analytics.orders, task_steps: extract, validate row counts, load, notify.