Fact table backfill without downtime
A bug in a transformation under-counted user watch-minutes for the last 90 days across a partitioned fact table (~3 TB/partition-day, 90 days). You must backfill the corrected values without taking the table offline for live dashboards, without double-counting, and without saturating the shared cluster that also runs daily pipelines and ad-hoc analyst queries. Design the backfill: how you reprocess 90 days correctly, how you swap in corrected data safely, and how you avoid starving production workloads.
What a strong answer looks like
Clarify scale and constraints first. Propose a clean component breakdown, then go deep on the hard parts (data model, bottlenecks, consistency, failure modes) and name the trade-offs you are making.
Clarify5:30 left
Estimate5:30 planned
Design16:30 planned
Deep dive13:30 planned
Failure9:00 planned
Which questions mattered is sealed until you submit. Telling you now would just be handing over the edge cases.
Run or narrate your approach, then ask the coach.