Data Quality Checklist
A checklist for keeping warehouse and lakehouse tables trustworthy. It covers ownership and expectations, freshness, volume, schema, nulls, uniqueness and valid values, referential and reconciliation checks, anomaly monitoring and handling data incidents, with a severity and a way to verify each check.
8
Sefism · Oct 2026
33
Sefism · Oct 2026
8
Sefism · Oct 2026
Data and ML
Sefism · Oct 2026
Checklist
Other
Sefism
17 min read
Intermediate
Free
What this checklist is
A checklist for keeping warehouse and lakehouse tables trustworthy. It covers ownership and expectations, freshness, volume, schema, nulls, uniqueness and valid values, referential and reconciliation checks, anomaly monitoring and handling data incidents, with a severity and a way to verify each check.
It has 8 chapters and 33 checks, each with how to verify it and how much it matters. For data engineers and analysts who own tables.
Chapter by chapter
1. Ownership and expectations (4 checks)
Checks need someone to act on them and a written standard to check against. Set these up before writing a single test.
2. Freshness (4 checks)
Stale data is the most common silent failure: every job succeeds while the numbers stop moving. These checks catch it.
3. Volume (4 checks)
A table can be fresh and still be missing half its rows. Volume checks catch partial loads, duplicated loads and filters that broke.
4. Schema (4 checks)
Upstream teams rename columns, change types and add fields without telling anyone. These checks stop schema changes at the door.
5. Nulls, uniqueness and valid values (4 checks)
The cheapest checks catch the most common bugs: duplicate keys from bad joins, missing required values and categories nobody expected.
6. Referential and consistency checks (4 checks)
Tables that are fine alone can still disagree with each other. These checks cover relationships between tables and agreement between related numbers.
7. Anomaly monitoring (4 checks)
Fixed rules catch the failures you predicted. Monitoring distributions over time catches the ones you did not.
8. Data incident handling (5 checks)
When bad data reaches people, the response matters as much as the fix. These checks borrow from Google's SRE incident practices and apply them to data.
How it works on Sefism
The checklist is one page: each check has a severity (must, should or nice to have) and a concrete way to verify it, and members can tick checks off as they go. The opening chapters are open to everyone. Adding it to your account uses credits, opens every chapter and keeps your progress.
Every claim is checked against the sources listed on the page (8 cited).
Ready to start?
Opens on Sefism in a new tab.
Stuck on something specific?
Writing only gets you so far. If you want an answer to your situation rather than the general case, book a session and we will work through it together. Sessions are free for approved Sefism members, and a few slots open each week.
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