Feature stores and platforms — the DoorDash arc, Airbnb's Chronon, Uber's Michelangelo — plus training-data pipelines and the AI-era stack.
MLOps is mostly data engineering. The proof is the feature-platform canon: DoorDash's Riviera→Fabricator→Workbench arc, Airbnb's Chronon (run at Stripe too), LinkedIn's Feathr, Uber's three Michelangelo generations, and the embedding-era stack — where interviews are heading as DE and ML infra converge.
Two-tower retrieval with its three silent-failure decisions, RAG stages named by the failure each fixes, vectors beside your metadata, and the debugging funnel that keeps any of it honest.
The demo was magic: ask the corpus a question, get a cited answer. Six weeks into production the complaints have a pattern — exact product codes return nothing (the embedding "understood" them into mush), a rephrased question retrieves different documents than its twin, and one answer confidently cited a paragraph that says the opposite. The team's fix list reads: bigger context window, better LLM, more chunks. None of those is the fix, because each failure lives in a *specific stage* of a pipeline the demo never made visible. Can you name the stage for each?
The full week 4 brief is part of LeetData Pro.
