Snowflake Data Quality Agent for Schema Drift, Null Spikes and Broken Dashboards

An enterprise analytics team was repeatedly dealing with broken dashboards, late data refreshes, schema changes and unexplained metric shifts in Snowflake. The team had tests and monitoring in some places, but alerts were fragmented and did not explain business impact.

The client needed a data quality agent that could do more than fire alerts. It had to detect the issue, explain what changed, identify downstream impact and route remediation to the right owner with evidence.

The challenge

The data platform had become business-critical. Sales, finance, operations and leadership teams depended on Snowflake-powered dashboards. When a table changed schema, a load was delayed or a key field became unexpectedly null, the issue often surfaced as a broken dashboard or an incorrect KPI.

The existing checks were useful but incomplete. Some checks found data issues without knowing who consumed the data. Some dashboard failures had no direct link back to the upstream table change. Some alerts lacked sample records, recent query history or recommended owners.

• Schema drift broke downstream models or BI extracts.
• Null spikes changed KPI values without clear explanation.
• Freshness issues were discovered by business users.
• Data quality tickets lacked table lineage and dashboard impact.
• Owners spent time finding the issue before they could fix it.

The solution

AgentFaktory designed a Snowflake data quality agent that combined deterministic checks, anomaly baselines, lineage impact and AI-assisted diagnosis. The agent did not modify data. It monitored, explained, prioritised and routed incidents.

The system tracked critical tables and measured schema, freshness, volume, null rate, uniqueness and referential patterns. When a check failed, the agent gathered context from query history, lineage metadata, load timestamps, recent transformations and downstream dashboards.

What was delivered

• Data quality rule catalogue for critical tables and columns
• Freshness, volume, null-rate, uniqueness and schema-drift checks
• Anomaly baseline logic for key datasets
• Lineage and dashboard-impact mapping
• Agent-generated incident summaries with evidence and suspected cause
• Ticket routing logic based on data owner and downstream severity

Example scenario

A revenue dashboard showed a sudden drop in one product category. Instead of asking an analyst to inspect the entire pipeline, the data quality agent identified a null spike in a product mapping column, linked the affected column to the revenue mart and showed which dashboard tiles were impacted. It then generated a ticket with sample SQL, affected tables, suspected upstream source and recommended owner.

Governance boundaries

The agent was deliberately kept out of autonomous production data changes. It could recommend a rollback, mapping correction, source-system check or steward review, but fixes required owner approval. This protected the platform from a common failure mode: letting automation hide data quality issues instead of making them transparent.

Checks implemented

The checks were organised by business criticality. Executive dashboards, customer-facing reports and operational marts received higher-priority monitoring. Lower-risk staging tables were still monitored, but alerting was tuned to avoid noise.

Agent behaviour

When a check failed, the agent built a diagnostic packet. It did not send a vague alert. The packet included the failed rule, affected table, affected columns, sample records or aggregate evidence, recent load history, downstream dashboards and likely owner.

This made the alert actionable. A data engineer could open the ticket and immediately see whether the issue was likely a source change, transformation error, delayed load, mapping problem or unexpected business event.

Operating model

• Critical datasets were assigned owners and escalation paths.
• Alerts were grouped to avoid one incident per failed column.
• Executive-dashboard impact increased ticket severity.
• The agent could recommend remediation but not modify production tables.
• Resolved incidents were captured as examples for future diagnosis.

Outcome

The client gained earlier visibility into data product failures and a better way to communicate impact. Instead of generic alerts, owners received evidence: what changed, where it changed, when it changed, which dashboards were affected and what remediation path was recommended.

The system helped move data quality from reactive dashboard firefighting to platform reliability management.

A useful data quality agent does not just say something is broken. It explains what broke, who is affected and what should happen next.

AgentFaktory role

AgentFaktory supported data quality rule design, Snowflake monitoring architecture, lineage impact modelling, agent diagnosis workflow and remediation-routing design.