SQL Validation and QA Reporting
Improved trust in downstream dashboards and reduced the time spent manually checking quality problems.
SQL and pandas-driven validation workflow for deduplication, missing field audits, outlier checks, and normalized reporting datasets.
What had to be solved
Reporting breaks when inconsistent upstream data is treated as good enough.
How the approach was shaped
Introduced validation rules and preprocessing layers before reporting outputs, reducing downstream ambiguity.
Technical thinking, feature focus, and delivery ownership.
The most valuable projects are never just a list of screens. This section shows how product, system, and process thinking came together.
- Data quality work positioned as a product of trust, not only a technical utility
- Duplicate detection
- Missing field audits
- Outlier flagging and normalized datasets
- Built validation and preprocessing routines
- Prepared clean datasets for dashboards and reporting
- Documented rule logic and workflow expectations
Product visuals and interface highlights.
Selected visuals that reinforce architecture, delivery scope, and product execution.
Validation workflow
QA flags, validation tables, and reporting-pack generation view.
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