Data Engineer
If you had to load a daily file from a partner into a warehouse, how would you make sure running the job twice doesn't create duplicate rows?
What the interviewer is looking for
- This question tests pipeline design & orchestration. Describes idempotent, restartable jobs with clear dependencies, retries, backfills and alerting in a named orchestrator.
- Strong answers: Idempotency through merge or upsert on a unique key, or overwriting a date partition, plus tracking which files were processed.
- Likely follow-up: What if the partner then sends a corrected file for last week?
Another way it may be asked: Your nightly load accidentally runs twice. What design stops you ending up with every row loaded two times?
A structure to follow: Answer, explain, example, limits
- Step 1
Answer
Give your direct answer in the first sentence so the interviewer knows where you are going.
- Step 2
Explain
Say how it works or why you would do it that way, in plain words before jargon.
- Step 3
Example
Anchor it in something you have actually done or seen, with the detail that shows hands-on experience.
- Step 4
Limits
Say when your approach would not be the right one. It shows judgement.
This is guidance on shape, not a script. Use your own real experience and facts; never invent details to fill a gap. See the STAR guide and answer frameworks.
Common slips
- Reciting a definition without showing you have applied it.
- Using terms you cannot explain if asked a level deeper.
- Claiming experience beyond what you can discuss in detail.
How to practise it
Say the answer in 90 seconds, then answer the follow-up as if it were a new question.
Related questions
- How do you structure dependencies and retries in an orchestration tool like Airflow or Dagster so that one failure doesn't cascade into a mess?
- Can you explain the difference between a fact table and a dimension table, and when you would use each?
- What checks would you put on a new data source before analysts start relying on it?
- Tell me about a time a Spark or similar distributed job was slow or failing. How did you find the cause and what did you change?
- Walk me through how you would choose between a cloud warehouse like Snowflake or BigQuery and a lakehouse setup for a new analytics platform.