“I Tried a Data Warehouse Testing Strategy. Here’s What Actually Worked.”

I’m Kayla, and I run data for a mid-size retail brand. We live in Snowflake. Our pipes pull from Shopify, Google Ads, and a cranky old ERP. This past year, I tried a layered testing plan for our warehouse. Not a fancy pitch. Just a setup that helped me sleep at night. And yes, I used it every day. If you want the unfiltered, step-by-step rundown of the approach, I detailed it in a longer teardown here.

Did it slow us down? A bit. Was it worth it? Oh yeah. For another perspective on building sleep-friendly warehouse tests, you might like this story about what actually helped me sleep.

If you want a vendor-neutral explainer of the core test types most teams start with, the Airbyte crew has a solid primer you can skim here.

What I Actually Used (and Touched, a Lot)

  • Snowflake for the warehouse
  • Fivetran for most sources, plus one cranky S3 job
  • dbt for models and tests
  • Great Expectations for data quality at the edges
  • Monte Carlo for alerts and lineage
  • GitHub Actions for CI checks and data diffs before merges

I didn’t start with all of this. I added pieces as we got burned. Frank truth.

The Simple Map I Followed

I split testing into four stops. Small, clear checks at each step. Nothing clever.

  • Ingest: Is the file or stream shaped right? Are key fields present? Row counts in a normal range?
  • Stage: Do types match? Are dates valid and in range? No goofy null spikes?
  • Transform (dbt): Do keys join? Are unique IDs actually unique? Do totals roll up as they should?
  • Serve: Do dashboards and key tables match what finance expects? Is PII kept where it belongs?

I liked strict guardrails. But I also turned some tests off. Why? Because late data made them scream for no reason. I’ll explain.

Real Fails That Saved My Neck

You know what? Stories beat charts. Here are the ones that stuck.

  1. The “orders_amount” Surprise
    Shopify changed a column name from orders_amount to net_amount without warning. Our ingest check in Great Expectations said, “Field missing.” It failed within five minutes. This would have broken our daily revenue by 18%. We patched the mapping, re-ran, and moved on. No dashboard fire drills. I made coffee.

  2. The Decimal Thing That Messed With Cash
    One week, finance said revenue looked light. We traced it to a transform step that cast money to an integer in one model. A tiny slip. dbt’s “accepted values” test on currency codes passed, but a “sum vs source sum” check failed by 0.9%. That seems small. On Black Friday numbers, that’s a lot. We fixed the cast to numeric(12,2). Then we added a “difference < 0.1%” test on all money rollups. Pain taught the lesson.

  3. Late File, Loud Alarm
    Our S3 load for the ERP was late by two hours on a Monday. Row count tests failed. Slack lit up. People panicked. I changed those tests to use a moving window and “warn” first, then “fail” if still late after 90 minutes. Same safety. Less noise. The team relaxed, and we kept trust in the alerts.

  4. PII Where It Shouldn’t Be
    A junior dev joined email to order facts for a quick promo table. That put PII in a wide fact table used by many folks. Great Expectations flagged “no sensitive fields” in that schema. We moved emails back to the dimension, set row-level masks, and added a catalog rule to stop it next time. That check felt boring—until it wasn’t.

  5. SCD2, Or How I Met a Double Customer
    Our customer dimension uses slowly changing history. A dbt uniqueness test caught two active rows for one customer_id. The cause? A timezone bug on the valid_to column. We fixed the timezone cast and added a rule: “Only one current row per id.” After that, no more weird churn spikes.

  6. Ad Spend That Jumped Like a Cat
    Google Ads spend spiked 400% in one day. Did we freak out? A little. Our change detection test uses a rolling 14-day median. It flagged the spike but labeled it “possible true change” since daily creative spend was planned that week. We checked with the ads team. It was real. I love when an alert says, “This is odd, but maybe fine.” That tone matters.

How I Glue It Together

Here’s the flow that kept us sane:

  • Every PR runs dbt tests in GitHub Actions. It also runs a small data diff on sample rows.
  • Ingest checks run in Airflow right after a pull. If they fail, we stop the load.
  • Transform checks run after each model build.
  • Monte Carlo watches freshness and volume. It pages only if both look bad for a set time.

I tag core models with must-pass tests. Nice-to-have tests can fail without blocking. That mix felt human. We still ship changes, but not blind.

The Good Stuff

  • Fast feedback. Most issues show up within 10 minutes of a load.
  • Plain tests. Unique, not null, foreign keys, sums, and freshness. Simple wins.
  • Fewer “why is this chart weird?” pings. You know those pings.
  • Safer merges. Data diffs in CI caught a join that doubled our rows before we merged.
  • Better trust with finance. We wrote two “contract” tests with them: monthly revenue and tax. Those never break now.

By the way, I thought this would slow our work a lot. It didn’t. After setup, we saved time. I spent less time chasing ghosts and more time on new models.

The Bad Stuff (Let’s Be Grown-Ups)

  • False alarms. Late data and day-of-week patterns fooled us at first. Thresholds needed tuning.
  • Cost. Running tests on big tables is not free in Snowflake. We had to sample smart.
  • Test drift. Models change, tests lag. I set a monthly “test review” now.
  • Secrets live in many places. Masking rules need care, or someone will copy PII by mistake.
  • Flaky joins. Surrogate keys helped, but one missed key map created bad dedupe. Our test caught it, but only after a noisy week.

Two Checks I Didn’t Expect to Love

  • Volume vs. Value. Row counts can look fine while money is way off. We compare both.
  • Freshness with slack. A soft window then a hard cutoff. Human-friendly. Still tough.

What I’d Change Next Time

  • Add a small “business SLO” sheet. For each core metric, define how late is late and how wrong is wrong. Post it.
  • Use seeds for tiny truth tables. Like tax rates and time zones. Tests pass faster with that.
  • Make staging models thin. Most bugs hide in joins. Keep them clear and test them there.
  • Write plain notes in the models. One-line reason for each test. People read reasons.
  • Still deciding between dimensional and vault styles? I compared a few options in this breakdown.

For a complementary angle on laying out an end-to-end warehouse testing blueprint, Exasol’s concise guide is worth a skim here.

I also want lighter alerts. Less red. More context. A link to the failing rows helps more than a loud emoji.

Who This Fits

  • Teams of 1–5 data folks on Snowflake and dbt will like this most.
  • It works fine with BigQuery too.
  • If your work is ad hoc and you don’t have pipelines, this will feel heavy. Start with just freshness and null checks.

Taking a step back, I know marathon debugging sessions can wreck any semblance of a social life. If you ever decide to balance late-night data fixes with a bit of after-hours fun, you can peek at FuckBuddies—the app matches consenting adults for straightforward, no-strings encounters so you can recharge and head back to your pipelines with a clear head.

Likewise, if you’re ever on a sprint in the Research Triangle area and want a similarly low-friction way to unwind, the local classifieds at Backpage Chapel Hill can quickly connect you with nearby, like-minded adults and spare you the time sink of swiping through mainstream apps.

Tiny Playbook You Can Steal

  • Pick 10 tables that matter. Add unique, not null, and foreign key tests.
  • Add a daily revenue and a daily spend check. Compare to source totals.
  • Set freshness windows by source. ERP gets 2 hours. Ads get 30 minutes.
  • Turn on data diffs in CI for your top models.
  • Review noisy tests monthly. Change warn vs fail. Keep it humane.

Final Take

I won’t pretend this setup is magic. It’s not. But