Data Quality Management Services | dbt Testing
Data quality problems compound silently. A source system changes a field definition, a pipeline drops null records, an ETL job fails halfway through and loads partial data. The report looks complete. The numbers are wrong. Decisions get made on bad information and the root cause stays buried until something important breaks.
We build data quality monitoring infrastructure that validates data as it moves through pipelines, alerts when anomalies occur before they reach reports, and gives the data team a clear view of what is trustworthy and what isn't. Schema validation, row count monitoring, statistical anomaly detection, and data lineage for the data layer that supports business decisions.
dbt tests for null values, referential integrity, uniqueness, and custom business logic, run on every pipeline execution
Row count and statistical anomaly detection that alerts when a pipeline delivers significantly more or fewer records than expected
Schema change detection that catches source system changes before bad data reaches the warehouse
Data lineage tracking from source to report so root cause investigation takes minutes, not days
Recent outcomes
Voice AI · Research
6× deeper insights
Text-based interviews converted to automated phone calls
AI Automation · Ops
20k+ txns day one
Manual invoice OCR across 40+ gas stations
Loyalty · Retail
1,062 users in 4 weeks
SuperValu & Centra loyalty platform with receipt validation
SaaS · Logistics
2,000+ shipments yr 1
Multi-carrier shipping hub for Indonesian eCommerce
The problem
How long does it take your team to find the root cause when a report shows a number that doesn't look right?
Has a pipeline ever silently loaded wrong data, no error, no alert, and the problem was discovered weeks later in a board meeting?
Short answer
Data quality management adds automated validation and monitoring to your data pipelines: dbt tests, row count and statistical anomaly detection, schema change alerts, freshness SLAs, and lineage tracking. Adding dbt testing to an existing project starts around $8,000 to $20,000; a full platform grows to $25,000 to $60,000. First version ships in 6 to 10 weeks at a fixed cost.
Key takeaways
Trusted by
Data quality management validates and monitors data as it moves through your pipelines, catching bad records before they reach a report. That matters because these failures stay invisible until something important breaks. A pipeline that silently drops records when a source field is null delivers a report that looks complete: the row count is close enough, the numbers are plausible, and nobody questions them until a decision made on that data produces a bad outcome. By then the root cause is buried in pipeline logs from three weeks ago, and finding it takes days.
The fix is data quality infrastructure that validates data as it moves through the pipeline, not after the fact, and not only when someone notices a number looks wrong. Schema validation before the load, row count checks after the load, statistical anomaly detection on the delivered data, and freshness SLAs that alert before a stale table reaches a report. We build that infrastructure as a defined engagement scoped to your existing pipeline and warehouse setup.
Poor data quality costs organizations an average of $12.9 million per year (Gartner). For most data teams, that figure compounds quietly: wrong decisions made on silent pipeline failures whose root cause never gets traced, and whose downstream impact never gets measured.
Capabilities
dbt schema tests written as SQL assertions that run at the end of every pipeline execution, before transformed data reaches analysts or dashboards. Built-in and custom tests catch nulls, duplicate keys, invalid values, broken foreign keys, and domain rules like non-negative revenue, with results stored in the warehouse so quality trends per table stay queryable rather than buried in CI logs.
Expected row count ranges established per table from historical delivery data, with alerts at configurable thresholds. This catches the partial-load failure mode, where a source timeout leaves the pipeline "successful" with a fraction of the records and the warehouse looks current but is materially incomplete. Freshness SLAs alert the moment a table goes stale.
Statistical anomaly detection applied to the values inside delivered data, not just row counts. Baselines built from history with seasonal decomposition flag deviations in metrics like daily revenue and transaction success rate. This catches semantic failures that pass every structural test, and the alert fires before the CFO sees a phantom revenue drop.
Schema change detection runs as a pre-load validation step: the current source schema is compared against a registry, and any discrepancy alerts before data loads with an unexpected structure. New, removed, renamed, and type-changed columns are each handled by per-table evolution policies, and a queryable change log answers "why did this model break six weeks ago?" in under two minutes.
Column-level lineage tracked from the raw source table through every transformation to the final mart and the BI measures that read from it. The graph enables forward impact analysis, the blast radius of a source schema change, and backward root cause analysis, tracing a wrong dashboard number to exactly where the value diverged.
Centralized data quality dashboard built in the BI tool your team already uses, pulling test results, anomaly history, freshness compliance, and schema changes into one queryable dataset. Every monitored table carries a named data owner, so alerts route to the right person, and a weekly summary replaces the ad-hoc "is the data trustworthy?" conversation with evidence.
Tell us your current pipeline setup, what broke last time bad data reached a report, and how long it took to find the root cause. We'll scope the monitoring infrastructure and give you a fixed cost.
Data Engineering Services, full data engineering capability overview
ETL Pipeline Development, pipelines that data quality monitoring validates
Data Warehouse Development, the warehouse layer where data quality is enforced
Real-Time Data Pipelines, streaming pipelines that also require quality monitoring
Stay on topic
Proof
UrShipper's fifth attempt at a multi-carrier shipping platform processed 2,000 shipments across 70+ countries in year one
Read the case study
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Read moredbt tests are SQL assertions that run against your warehouse tables after each pipeline load. A not_null test runs a query that counts null values in a column, if the count is above zero, the test fails. A unique test finds duplicate values. Custom tests run any SQL you write: 'revenue must be positive', 'order date cannot be in the future', 'every order must have a matching customer'. Tests run automatically at the end of each dbt run. When a test fails, the run produces an error with the failing records, and the data team is alerted before anyone queries the affected table.
Testing (dbt tests, schema validation) checks specific assertions about the data: 'this column cannot be null', 'this value must exist in a reference table'. Monitoring (row count, anomaly detection, freshness) tracks statistical properties of the data over time and alerts when something deviates from the expected pattern without a pre-defined assertion. Testing catches known failure modes. Monitoring catches unknown failure modes, the pipeline that delivers 40% fewer records than usual because a source system had an issue, without failing any specific test. A complete data quality system uses both.
Freshness SLAs are configured by table: each table gets an expected update frequency (every 1 hour, every 24 hours, every Monday at 6am) and a tolerance window before alerting. dbt's built-in source freshness check compares the maximum timestamp in a configured column against the expected freshness. For tables without a reliable timestamp column, we build a pipeline metadata table that records the completion time of each pipeline run and monitor against that. Alerts go to Slack or PagerDuty depending on the severity of the affected table.
Adding dbt-based testing and freshness monitoring to an existing dbt project starts around $8,000 to $20,000. A full data quality platform including anomaly detection, schema change monitoring, lineage tracking, and a quality dashboard grows to $25,000 to $60,000. Fixed cost agreed before development starts.
Work with us
We scope Data Quality Management in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.