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Data Quality Assurance

Your dbt tests pass.
That doesn't mean your data is right.

Qualibri Tech helps data teams find and reduce data quality risks before they reach production. We start with a diagnostic to show you where the risks sit and what to fix first.

Book a 30-minute scoping call

No pitch deck. You bring context, we tell you what we see.

Proven results

270+ defects found & fixed before productionRegulated data platform on dbt + Snowflake
90% fewer release-blocking defectsReal-money iGaming & iLottery platform
Regression validation: from 5–7 days to < 1 hourRunning hourly in production
850 repeatable checks automatedSaving ~12–15 hours per release cycle
5 parameterized tests → 100 test cases in under 5 minutesReplacing a week of manual validation

The problem

The worst data failures are the silent ones.

The pipeline runs green. The tests pass. Then a VP opens a dashboard and asks why the number is wrong.

Nobody remembers the 40 pipelines that ran correctly that morning. Everyone remembers the one number that was off. That is how data teams lose trust: not through outages, but through silent failures that stakeholders find first.

More dbt tests will not fix this on their own. Most teams already have hundreds of tests, written once and rarely touched again. They check columns nobody worries about, while the riskiest flows run unwatched.

The problem is not effort. It is visibility. Knowing which data flows carry the most risk is a diagnosis problem, and it comes before any fix.

This is where Qualibri steps in.

Our approach

We diagnose before we fix or automate.

Most vendors start with a tool or a headcount. Qualibri starts with a question: which of your data flows would hurt the business most if they were quietly wrong?

We will not push a tool on you, and nothing gets automated before we understand what is at risk. Code-based automation comes later, where the evidence says it pays off, on your existing stack.

  • SQL
  • Python
  • dbt
  • Snowflake
  • Airflow
  • AWS
  • Azure

The Data Trust Assurance Framework

Qualibri follows a proprietary 7-step method, from first discovery to ongoing maintenance and improvement.

  1. 01Discover
  2. 02Assess
  3. 03Prioritize
  4. 04Validate
  5. 05Automate
  6. 06Sustain
  7. 07Maintain & Improve

How it works

Start with the Data Trust Diagnostic

A structured data quality risk assessment. 2–4 weeks, fixed-fee. It covers the first stages of the Data Trust Assurance Framework, so you see the whole path up front and decide: continue with us, hand the fix list to your own team, or stop there.

What we assess

  • Current critical data flows
  • Existing test coverage
  • Coverage gaps
  • Data quality risks
  • Manual validation effort
  • Automation readiness
  • Ownership and maintenance gaps
  • Tooling fit

What you get

  • Critical Data Flow Map
  • Current Test Coverage Review
  • Coverage Heat Map
  • Data Quality Risk Register
  • Prioritized Fix List
  • Automation Readiness View
  • 30/60/90-Day Roadmap
  • Executive Summary

Not sure what your current tests actually catch?

Book a scoping call. We will tell you which flows we would map first and why — and honestly, whether the Diagnostic is worth it for your platform.

Book a 30-minute scoping call

Qualibri vs. the alternatives

Not a QA firm. Not another tool.

Qualibri Tech compared with traditional QA firms and observability tools, across where the work starts, complex business logic, prioritization, tooling, and what you keep.
Traditional QA firms Observability tools Qualibri Tech
Where it starts Executing the test plan you hand them Alerting on anomalies after they happen Diagnosing which flows carry real risk
Complex business logic Depends on the brief they are given Out of scope beyond unit-level and simple integrity checks The core focus, validated on your real data
Prioritization By the task list By whatever alerts loudest By business impact
Tooling Whatever they staff for One more platform to buy, configure, and maintain Your existing stack, tool-agnostic
What you keep Executed test cases Dashboards someone still has to tune Risk register, coverage heat map, fix list, roadmap

We diagnose first, prescribe second, and build third.

Our work

Results from real engagements.

dbt + Snowflake
Regulated data platform

270+ defects found and fixed before production. An automation proof of concept turned 5 parameterized tests into 100 test cases running in under 5 minutes — validation work that previously took about a week.

Read the case study
iGaming & iLottery
Real-money gaming platform

Data quality built from the ground up for a complex data platform with millions of bets an hour. Release-blocking defects dropped 90%. 850 repeatable checks automated. Regression validation went from 5–7 days to under 1 hour.

Read the case study
Clinical development
Clinical analytics platform

The unit tests passed, but the dashboards didn't. Qualibri ran the diagnostic and replaced fragmented testing on a growing analytics platform with one risk-based strategy. The Tableau reporting layer got its first automated coverage.

Read the case study

Testimonials

What our clients say.

Qualibri played a key role in establishing quality engineering for one of our most complex data platform initiatives. What distinguishes them is their ability to understand the real business risks behind technical challenges. Rather than simply executing tests, they identify the areas of highest impact and help prioritize what should be tested first.
Michael Triger
Data Engineering & BI Director
Qualibri understood almost immediately where our major pain points stem from and helped us get oversight on all process steps and their test coverage. They bring a ton of experience and a deep understanding of what resources are required in the process.
Volker Rothenbacher
Senior Advisor Medicine, Data, AI & Systems — Digital Transformation
Whether dealing with complex data quality issues, defining testing strategies, or validating business-critical rules, Qualibri Tech always brought structure, transparency, and practical solutions. Their attention to detail significantly reduced project risks and increased confidence in delivery outcomes.
Natalia Zhornyk, founder of Qualibri Tech
Natalia ZhornykFounder at Qualibri Tech

Meet the founder

“I'm Natalia, founder of Qualibri Tech. I help data teams find and reduce data quality risks before they reach critical reports, data-driven releases, AI outputs, or production.

Many teams already have tests, checks, and dashboards in place. But they still don't know which critical flows are under-tested, which checks catch real business failures, or what should be fixed first.

Bad data stays invisible until a VP spots a wrong number in a board deck or a customer reports a billing error.

Qualibri Tech helps prevent that.”

Natalia is the person on your scoping call. She is also the one who assesses your data flows and stands behind the findings.

Book a scoping call with Natalia

FAQ

Frequently asked questions.

We already use Metaplane / Monte Carlo / Soda to catch silent data quality issues. Why do we need Qualibri Tech?
Data observability tools are only as good as the checks someone programs into them, and using their full power takes testing expertise most teams don't have in-house. Out of the box, they mostly run unit-level and simple data integrity checks. Validating complex business logic is a different level of expertise: it takes human knowledge of your data flows to decide what deserves a check at all. That is what the Diagnostic delivers — what should be tested, whether your coverage matches your business risk, and what to fix first. It often makes the tool you already own more valuable, because it finally gets pointed at the right things.
What exactly do we get at the end of the Diagnostic – a report, or something actionable?
The Diagnostic produces working documents, not a slide deck. You get a data quality risk register, a coverage heat map showing what is tested and what is unguarded, a prioritized fix list, your score on the Data Trust Maturity Model (our 5-level scoring framework), and a 30/60/90-day roadmap with an executive summary. These are files your team keeps and references long after the engagement ends.
How long does it take? We can't absorb a 6-month engagement.
The Diagnostic takes 2 to 4 weeks, depending on the scope of your critical flows. It runs alongside your normal delivery work, so nothing pauses while we assess.
We already have dbt tests. Why isn't that enough?
dbt native tests mostly check schema: not-null, unique, accepted values. They rarely validate business logic, cross-table consistency, or freshness on the flows that matter. That is how every test passes while a dashboard shows the wrong number. The Diagnostic measures exactly this gap between what your tests check and what can actually fail.
Are you just going to tell us to buy a tool?
No. Qualibri is tool-agnostic, and we will not recommend new tooling until we understand your current coverage, risks, and stack. Sometimes the honest recommendation is to get more from what you already run, and sometimes it is to buy nothing yet.
What do you need from our team?
Read access to your repositories and warehouse, and a few hours of stakeholder time to walk us through business logic and context. That is it. If access or context is genuinely impossible to provide, the Diagnostic will be weak, and we will tell you so on the scoping call rather than after.
We have an internal data team. Won't this look like a critique of their work?
The Diagnostic maps risk, not blame. In practice, most findings confirm what your engineers already suspected but could not prioritize or get buy-in for. A written risk register with evidence is what finally gets those items onto the roadmap.
Will this slow down our release?
No. The Diagnostic runs alongside delivery, and if a release is coming, it tells you what to validate before go-live instead of after. The goal is fewer surprises in production, not more process in front of it.
Do you actually know our stack, or is this theory?
The work happens in your repositories, on your real data: SQL, Python, dbt, Snowflake, Airflow, Azure, AWS, and other tools. The results above were delivered on these stacks by the founder you will talk to. Bring your hardest pipeline question to the scoping call.
What if we don't want to continue after the Diagnostic?
Then you stop, and everything stays yours. Teams use the deliverables to fix things internally, hire with clarity, buy a tool with actual justification, or deliberately postpone work because the risks turned out to be low. The Diagnostic is designed to be complete on its own.
What happens after the Diagnostic? Can you help with the implementation?
Yes. The Diagnostic is the entry point of the Data Trust Assurance Framework. Most teams continue into a testing strategy and a focused automation MVP: 1-3 critical flows automated on your real data, to prove value before anything scales. From there, Qualibri can build out coverage and, if you want, maintain and improve the whole system on an ongoing basis. If you continue with us, part of the Diagnostic fee is credited toward the next phase.

Find out what your tests actually protect.

Book a 30-minute scoping call. You bring your stack and your current worries. We tell you honestly whether the Data Trust Diagnostic fits your situation. If it does not, you leave with sharper questions to ask your own team.

Book a 30-minute scoping call

Direct with the founder. No sales sequence afterwards.