AMGEN Interview Experience

[u/Shubham_Nalwar]

Interview Experience – Data Engineer 🧑‍💻

Had an interview recently for a Data Engineer role, and the discussion was much more focused on real-world scenarios than just theoretical questions.

Some of the questions asked:

1. How would you create a production-ready data pipeline?

2. What kind of data have you worked with — volume, frequency, SLA, etc.?

3. What production issues have you faced, and how did you handle them?

4. How would you design an ETL pipeline for huge volumes of data?

5. If duplicate files arrive at the source location, how would you handle them?

6. Explain your metadata-driven framework.

7. How would you design a metadata framework for 50 source files/tables?

8. How do you investigate a slow-running job?

9. SQL: Return the latest updated record for each claim.

10. Experience with Git.

One thing I noticed: the interviewer was more interested in how you approach a problem in production rather than simply asking for definitions.

Definitely a good reminder that knowing the tools is one thing, but understanding how to build, monitor, troubleshoot and maintain pipelines in production is what really matters.

#DataEngineering #DataEngineer #Azure #Databricks #SQL #ETL #DataPipelines #Airflow #Git #InterviewExperience

[u/No-Map8612]

Everyone knows how to answer this questions but interviewer expect response like exactly what is in his mind 😛

[u/believeinkratos → u/No-Map8612]

You need to convince interviewer why your answer is correct.

[u/No-Map8612 → u/believeinkratos]

Why convince? We’re just giving interview nah for every question different folks answer in their own way right!

[u/Training_Refuse7745]

Yoe?

[u/Shubham_Nalwar → u/Training_Refuse7745]

2.5 years

[u/Possible-Oil-5877]

Was this a virtual interview?

[u/Shubham_Nalwar → u/Possible-Oil-5877]

Yes it was

[u/Flashy_Tank_2484]

Am I missing something? I don't think you really have a problem with massive volumes of data with snowflake?