An Airflow skills test checks whether a data engineer can design, schedule, monitor and repair data pipelines in Apache Airflow, the open source workflow orchestration tool. Strong tests use real tasks: write a DAG with dependencies and retries, debug a failed run, and explain how to backfill safely. Glider AI’s Technical Assessments support this kind of hands on, role based test.
Airflow shows up on a lot of resumes. Many candidates have triggered a DAG someone else wrote. Far fewer have designed pipelines that recover cleanly when an upstream source is late, a task fails halfway, or a schema changes overnight. The difference matters, because your dashboards, reports and machine learning features all depend on those pipelines running on time.
What Airflow work looks like on the job
Apache Airflow lets engineers define pipelines as Python code, called DAGs (directed acyclic graphs), made of tasks with dependencies, schedules and retry rules. Teams run it self hosted, on Kubernetes, or through managed services such as Cloud Composer on Google Cloud and Amazon MWAA.
Day to day, an Airflow engineer:
- Writes and maintains DAGs that move and transform data
- Chooses the right operators, sensors and hooks for each source
- Sets schedules, retries, timeouts and alerts
- Debugs failed runs from logs and task states
- Runs backfills without duplicating or corrupting data
- Keeps pipelines readable for the next person on call
Skills to assess by role
| Role | What to test | Suggested task |
|---|---|---|
| Junior data engineer | Python basics, simple DAG structure, task dependencies | Write a three task DAG from a short spec |
| Mid level data engineer | Operators, sensors, retries, idempotent tasks, scheduling | Fix a DAG that fails on rerun |
| Senior data engineer | Pipeline design, backfills, performance, testing, deployment | Design review of a multi source pipeline |
| Analytics engineer | SQL transforms orchestrated by Airflow, data quality checks | Add a quality check task and alert |
| Platform or MLOps engineer | Airflow infrastructure, scaling, environments, secrets | Explain how to run Airflow across dev and prod safely |
How to structure an Airflow skills test
Start with fundamentals
A short set of multiple choice and scenario questions covers scheduling concepts, how retries and timeouts interact, what makes a task idempotent, and when to use a sensor versus a scheduled check. This filters out keyword matches quickly.
Add a hands on coding task
Ask candidates to write or complete a DAG in Python against a clear requirement. Score readability, correct dependencies, sensible retries and whether rerunning the DAG produces the same result. A coding simulation environment lets candidates work in code instead of describing it.
Finish with failure scenarios
Give a failed run’s logs and task history and ask what went wrong and how to recover. Then ask how they’d backfill a week of missing data. Senior engineers answer with specifics about idempotency and downstream impact. Less experienced candidates usually jump straight to “rerun it”.
Red flags in Airflow candidates
- Treats Airflow as a place to do heavy data processing rather than orchestration
- Writes tasks that produce different results when rerun
- Has no answer for monitoring or alerting
- Can’t explain how they’d test a DAG before it reaches production
- Describes only tutorials, not pipelines they’ve maintained
How Glider AI helps you hire data engineers
Glider AI’s Technical Assessments cover 250+ technologies, so you can combine Airflow, Python, SQL and cloud questions in one test that matches your data stack. Hands on coding simulations show how a candidate actually builds, and open ended answers are scored against a rubric with a human override.
Integrity matters for remote technical hiring. AI Proctoring records tab switching, copy and paste, and face detection events for recruiter review, and questions are watermarked per candidate. For broader context on DevOps style delivery skills that often sit next to Airflow, see Glider AI’s guide to CI/CD.
The point is not to make the process longer. A focused test replaces a round of technical phone screens, so hiring managers spend interview time on candidates who have already shown they can build and repair pipelines. That is the practical case for skills based hiring in data teams.
Frequently asked questions
What skills should an Airflow engineer have?
An Airflow engineer should write clear DAGs in Python, choose suitable operators and sensors, set schedules, retries and timeouts, make tasks idempotent, debug failed runs from logs, run safe backfills, and set up monitoring and alerts. Senior engineers also design pipelines across many sources and manage deployment.
How do you test Apache Airflow skills before hiring?
Use a short fundamentals check, then a hands on task where the candidate writes or fixes a DAG in Python, followed by a failure scenario built from real logs. Score each part with a rubric covering correctness, rerun safety and readability, and proctor the session.
Is Python required for Airflow roles?
Yes. Airflow pipelines are defined in Python, so candidates need solid Python skills, including functions, modules, error handling and working with APIs and databases. Test Python alongside Airflow concepts rather than separately, since the two are used together on the job.
What is the difference between Airflow and Cloud Composer for hiring purposes?
Cloud Composer is Google Cloud’s managed Airflow service, so the core DAG skills are the same. Candidates who’ve used Composer may know Google Cloud networking, permissions and environment setup better. Test core Airflow skills first, then add cloud specific questions if your team runs Composer.
How long should a data engineering skills test take?
Most data engineering assessments run 45 to 60 minutes. Junior roles can use a shorter test with one coding task. Senior roles need extra time for design and failure scenarios. Focused tests keep completion rates higher with experienced engineers.
Can you use the same test for data engineers and analytics engineers?
Partly. Both need orchestration basics, but analytics engineers lean on SQL and data quality checks, while data engineers need deeper Python, pipeline design and infrastructure skills. Share a fundamentals section and tailor the hands on tasks to each role.
Hire data engineers who keep pipelines running
Pipeline failures are expensive and quiet until someone notices a broken report. Build a role specific Airflow skills test with Glider AI’s Technical Assessments and confirm data engineering skills before the first interview.