Apache Airflow Interview Questions: Data Orchestration Deep Dive
Everything you need to know about Airflow for data engineering interviews — DAGs, operators, scheduling, best practices, and common gotchas.
Key Takeaways
- ✓Airflow in the Modern Data Stack
- ✓Core Concepts to Know
- ✓Advanced Topics
Airflow in the Modern Data Stack
Apache Airflow is the most popular data orchestration tool. If a company uses Python-based data pipelines, they almost certainly use Airflow (or a managed version like Cloud Composer or MWAA).
Expect Airflow questions in any company that values pipeline reliability and observability.
Core Concepts to Know
- DAGs, operators, and tasks
- Sensors and their use cases
- XComs for task communication
- Connections and hooks
- Executor types (Local, Celery, Kubernetes)
- Trigger rules and branching
- Backfill and catchup behavior
Advanced Topics
For senior roles:
- Dynamic DAG generation patterns
- Custom operators and plugins
- Airflow 2.x TaskFlow API
- Scaling Airflow (KubernetesExecutor, auto-scaling workers)
- Monitoring and alerting strategies
- CI/CD for DAGs
Reviewed by Aditya Kumar · DataEngPrep Editorial Team
Drafted by the editorial team and signed off by Aditya Kumar, founder and lead editor at DataEngPrep. Questions are sourced from real interviews, initial answers are drafted with AI assistance, and every section is human-edited for technical accuracy, relevance to current FAANG hiring rubrics, and clarity. Articles are reviewed periodically as interview patterns evolve.
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