Problems to solve first: Duplicate feature work, online/offline inconsistency, and training-serving skew (point-in-time misalignment). The platform's value is these three things.
Dual storage: Offline store (data warehouse / lakehouse, time-partitioned history for training and backfills) plus online KV (low-latency point lookups for inference). One feature definition, written to both.
Point-in-time correctness: When building training samples, you must look up the feature value as of the sample timestamp - otherwise you leak the future. This is the core and hardest part to explain well.
Governance: feature registry and lineage, versioning, freshness SLAs, and reuse rate. Interviewers want to hear not just storage, but who uses it and how you prevent it from rotting.
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