Data Engineer Interview Guide

Data Engineering Interview Questions

Screen data engineers on pipeline design, modeling, orchestration, data quality, and production-grade analytics infrastructure.

Strong data engineers are operators, not just SQL writers. These prompts test pipeline reliability, schema evolution, idempotency, and how candidates think about data quality at scale.

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Section 1

Pipeline Design

Test orchestration, idempotency, and recovery.

1. Design an ELT pipeline that ingests events from Kafka into a warehouse with exactly-once semantics.

Interview prompt

Strong signal

Discusses idempotent writes, dedupe keys, backfills, schema registry, and monitoring.

Follow-up probes

  • •How do you handle late-arriving events?
  • •How do you backfill safely?

Red flags

  • •Ignores duplicates.
  • •No backfill or replay strategy.
Section 2

Modeling

Evaluate dimensional modeling, slowly changing dimensions, and analytics access patterns.

1. When would you choose a star schema over a wide flat table for analytics?

Interview prompt

Strong signal

Discusses query patterns, governance, BI tool behavior, and update cost.

Follow-up probes

  • •What are SCD type 2 trade-offs?
  • •How would you handle late dimension updates?

Red flags

  • •Treats one model as universally better.
  • •No discussion of access patterns.
Section 3

Data Quality

Check whether candidates treat data quality as a first-class concern.

1. A downstream dashboard shows wrong revenue numbers. Walk me through your investigation.

Interview prompt

Strong signal

Reasons across ingestion, transformation, modeling, and consumption layers; uses lineage and tests.

Follow-up probes

  • •How would you prevent recurrence?
  • •What tests belong in CI?

Red flags

  • •Skips lineage thinking.
  • •No prevention plan.

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