The interesting part of Data Migrations is not the checklist itself. It is the moment when the team realizes a quick pass and a trustworthy pass are not the same thing.
When I review work in Data Migrations, I am not only asking whether the ticket appears complete. I am asking whether the evidence, code behavior, and surrounding assumptions fit together tightly enough that I would trust the result after release. That difference matters because the schema update succeeds while the meaning of old records quietly shifts underneath it.
The review becomes useful when it tests the story behind the result, not just the result itself.
The First Signals I Look For
- Does the implementation clearly support integrity, backfills, reversibility, and watching data change safely?
- Is the risky path visible, or has it been left to assumption?
- Would another reviewer understand the user impact without extra verbal explanation?
Questions I Ask Before I Call It Ready
I ask what changed outside the happy path, what happens under interruption, and how the team would know it failed in real use. With Data Migrations, those questions matter because a migration finishes on time but leaves a small slice of historical data partially transformed.
I also want to know whether the work can be explained to database owners and teams carrying historical data forward without hand-waving. If the answer needs too much translation, there is often still a hidden gap.
What Good Evidence Looks Like to Me
Good evidence is easy to point to and hard to misunderstand. For this topic I am looking for something like sampled before-and-after records, fallback steps, and checks for partial completion.
I hold the review when the result depends on a promise nobody verified, when a negative path was skipped because it seemed unlikely, or when the notes only show activity instead of meaning. When the conversation gets better, the testing usually gets faster as well.