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Data quality

Contracts, freshness, and when to fail the job

5 questions with solutions

  1. Q1AirbnbNetflixUber

    Four checks you put on every gold table first.

    Solution

    Freshness, volume (not zero, not 10×), schema, uniqueness of the grain. Everything else is extra. 400 noisy tests train people to ignore red.

  2. Q2StripeCapital OneShopify

    Fail the pipeline or open a ticket?

    Solution

    Fail on grain, uniqueness, and PII contract breaks. Ticket on a soft anomaly you cannot yet explain. Write the policy down. “It depends” without a rule is a fail.

  3. Q3DatabricksSnowflakeMcKinsey

    dbt tests vs Great Expectations vs Monte Carlo.

    Solution

    dbt for model grain you own. GE/Soda for richer expectations and non-dbt paths. A vendor when silent breakage across hundreds of tables is the incident. Start with tests; buy when misses are expensive.

  4. Q4UberLyftDoorDash

    Where should the contract live — producer or warehouse?

    Solution

    Producer owns the schema and SLA. Warehouse tests are defense in depth. A contract only in dbt is how source teams ship breaking changes on Friday.

  5. Q5MetaAmazonGoogle

    A check is red every day and nobody cares. What did you do wrong?

    Solution

    The threshold is wrong or the owner is missing. Fix or delete. A permanently red test is worse than no test — it trains the team to ship on red.

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