Data Engineering & AI Interview Playbook

  Why Debugging Matters More Than Building Pipelines In production systems, pipelines rarely fail cleanly. More often, they succeed with incorrect data , which is far more dangerous. Most senior Data Engineering interviews today include debugging scenarios , not just “how would you build X”. Below are real situations Data Engineers face—and how to debug them correctly. Scenario 1: Pipeline Succeeded, but Dashboard Numbers Are Wrong Problem A daily pipeline ran successfully, but: Revenue numbers are inflated User counts are higher than expected No job failures or alerts Common Root Causes Duplicate ingestion Incorrect joins Missing deduplication Late-arriving data processed twice How to Debug Compare row counts between raw and transformed tables Check if data for the same date was ingested more than once Validate join keys (many-to-many joins are common culprits) Check incremental logic (e.g., updated_at filters) Example Fix If d...

  1. Why GenAI Matters to Data Engineers (Not Just ML Engineers) Generative AI systems are no longer experimental add-ons; they are becoming first-class consumers of data platforms . While ML Engineers focus on model selection and training, Data Engineers are responsible for the data foundations that make GenAI systems reliable, scalable, and trustworthy . So Data Engineering acts as strong foundation for GenAI systems. From chatbots to internal AI assistants, GenAI applications depend heavily on: Clean, well-structured data Reliable ingestion pipelines Low-latency access to relevant information This means Data Engineers do not need to become ML experts—but they must understand how their data systems support AI workflows . 2. What Data Engineers Do NOT Need to Know Let’s clear a common misconception. Data Engineers are not expected to: Train large language models Tune neural network hyperparameters Implement backpropagation or transformers Compete with ML Engineers or researchers...