ETL and data pipeline testing before bad data reaches the dashboard
Source-to-target reconciliation, transformation logic, freshness and data quality, verified on every pipeline change by a QAShift engineer.
When a pipeline breaks loudly, someone fixes it. When it breaks quietly, dashboards show wrong numbers, machine learning models train on bad data and leadership makes decisions on figures that are off by a few percent.
QAShift tests pipelines the way it tests applications: every change is checked against expected outputs, with row counts, sums and sample-level comparisons from source to target.
What our etl and data pipeline testing covers
Source-to-target reconciliation
Row counts, sums and keys match between source systems and the warehouse.
Transformation logic
Joins, aggregations and business rules produce the expected outputs.
Data quality rules
Nulls, duplicates, ranges and formats stay within agreed tolerances.
Schema drift
Upstream column changes are detected before they break downstream models.
Freshness
Data lands on schedule, and late or partial loads are flagged.
Lineage
Failures are traced back to the step and source that caused them.
What it catches before your customers do
- A join change doubles revenue in the board dashboard
- An upstream column rename nulls a field across every report
- A partial load runs and nobody notices the missing day
- Duplicate events inflate active-user metrics
Two ways to get it done
Run it yourself
Pipeline testing is delivered on managed plans, where an engineer learns your data model and sources.
We run it for you
Your engineer writes reconciliation and quality checks for your pipelines, runs them on every change and reports issues with the exact rows affected.
What you get
- Reconciliation and quality checks
- Row-level diffs for failures
- Freshness and schema-drift alerts
- Pipeline status in your morning report
Frequently asked questions
Which data tools do you work with?
Common warehouses and orchestration tools, including dbt-based pipelines. Scope is agreed during onboarding.
How is this different from dbt tests?
dbt tests check rules inside a model. QAShift adds source-to-target reconciliation across systems, business-rule validation and an engineer who investigates failures.
Do you test streaming pipelines?
Yes, with checks on completeness, ordering and lateness over agreed windows.
Is this available self-serve?
No. Data pipeline assurance is part of managed plans.
Ship without fear.
Start testing free in minutes, or let a QAShift engineer deliver your first morning report within 5 days.