Case study · US Upstream Operator
Data quality framework
Bad data discovered only when a report looked wrong.
Problem
Bad data discovered only when a report looked wrong.
Approach
Designed, developed, and implemented a Snowflake-based data quality framework for an energy operator's critical reporting views, surfaced through Sigma dashboards. Instead of discovering bad data when a report looks wrong, stakeholders get continuous visibility: rule-based checks running over the reporting layer, with results tracked and trended on dashboards the business actually opens. Worked with stakeholders through requirements, validation, and adoption - because a DQ framework nobody checks is just compute cost.
Architecture
Data quality as a product, not a project
One-off data audits decay immediately. This framework treats quality as continuously measured: define rules where the business feels pain, run them on schedule, trend the results, and make ownership visible. The dashboard is the accountability mechanism - when a critical view degrades, it's seen the same day, not at month-end close.