From a failed data check to the on-call phone

When a critical data quality check fails in the middle of the night, a Slack alert that no one reads until morning is too slow. Super Technologies needed its data team to treat data incidents the way engineering teams treat production outages. In this session, Nikola Kljajo shows how the company uses DataHub as the context layer for its data stack, bringing catalog, lineage, metric definitions, ownership, and data quality together across Snowflake tables and Tableau dashboards. He walks through the data quality workflow his team runs in production, from the assertions it monitors in DataHub to an incident.io integration that pages the on-call engineer as soon as a high-priority assertion fails.