Download our Data Engineering Question Bank to learn the questions top companies ask when hiring Data Engineers in the AI era.
Omkar Deshpande
Head of Curriculum
Frame your responses to show how you build scalable, AI-ready data systems.
Get questions on data pipelines, real-time streaming, data lakes vs. warehouses, feature stores, and AI-driven data infra.
Expect problem-solving questions on model-data drift, data lineage, and large-scale processing.
Show conceptual mastery of distributed systems, not just tool familiarity.
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ML Tech Lead—Deep Learning & GenAI.Currently leading a team driving 0 to 1 AI‑powered personalization efforts for conversational commerce.
Ex-Amazon, ex-Twitter ML expert. Home feed, Reply recommendations, Marketplace optimization, and more.
Co-author, Sculpting Data for ML A practical guide to curating quality datasets that lay a strong foundation for an ML pipeline.
Program Committee at Leading AI Conferences Worked for conferences such as ICML, KDD, SIGIR, TheWebConf, ICDM, and more.
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Depth Over Tools: Go beyond “which tech stack you know”—show problem-solving and AI data pipeline expertise.
AI + Data Fusion: Handle questions where data engineering meets machine learning systems.
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Download to gain insights into the questions AI Software Engineers face in FAANG+ interviews in the era of AI.
Strategic Interview Thinking: Learn how to frame your responses by understanding what top companies really test for in modern AI-driven data engineering roles.
Core Technical Focus Areas: Discover key topics—like real-time data pipelines, data lakes, feature stores, and MLOps—that repeatedly show up in top interviews.
More than 150 Questions to give you in depth understanding
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