Data Engineer Interviews don’t fail on tools

Published 2026-02-07 in PySpark

🚨 Data Engineer Interviews don’t fail on tools. They fail on depth. I’ve seen this again and again 👇 Most candidates say: ❌ “I used Spark, Airflow, AWS” But interviewers really want to hear: ✅ “Why this design?” ✅ “How do you handle failures?” ✅ “What happens at scale?” So I curated a list of most-asked Data Engineer interview questions — the kind companies like Persistent, Walmart, Infosys, product firms actually ask. 📌 This covers real interview areas: • End-to-end ETL & Data Pipeline design • Spark internals & performance tuning • Batch vs Streaming trade-offs • SQL problem-solving mindset • Data quality, retries & idempotency • Airflow DAG design & failure handling • Real production scenarios (not theory) 💡 Interview truth: Anyone can write code. Very few can explain decisions like an engineer. If you can clearly explain: Source → Ingestion → Processing → Storage → Consumption → Failure handling you instantly stand out from 80% of candidates. 👥 This post is for: • Data Engineers (3–8 years) • SQL + Spark learners • Anyone preparing for product-based companies

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