Data Engineering & AI Interview Playbook

  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...