Meta Data Engineer interview questions typically reflect the company’s product-scale priorities: you’ll be evaluated on translating product metrics into reliable pipelines, designing scalable data models, and optimizing queries and ETL for very large datasets. Expect a strong emphasis on SQL (window functions, CTEs, joins and aggregation), Python for scripting and data transformations, and system-design thinking for streaming and batch architectures. Interviewers often probe trade-offs around latency, cost, reliability, and data quality, along with your ability to communicate assumptions and trade-offs clearly. For interview preparation, plan for an initial recruiter screen followed by one or more technical screens and a full loop that mixes SQL/Python coding, data-modeling or pipeline design, and behavioral ownership questions. Prep by practicing timed SQL and Python problems, sketching end-to-end pipeline designs, reviewing partitioning/indexing and performance tuning, and preparing STAR stories that show impact and collaboration. During interviews, ask clarifying questions, think aloud, and be ready to discuss real-world constraints—you’ll be judged as much on clarity and trade-off reasoning as on raw syntax.