Demonstration project using synthetic data, not a client engagement. A local Python workflow validates a JSON export, normalizes numbers and dates, removes identical duplicates, and separates invalid rows with clear reasons. Conflicting records stop the run instead of silently overwriting data. Verified example: 16 input rows → 12 accepted records, 1 duplicate and 3 rejected rows. Nine automated tests passed. Original files and existing output folders are preserved. Deliverables prepared for this demonstration: a Python script with tests, CSV/JSON results with an audit summary, an editable Excel dashboard with four charts, and Word/PDF technical documentation. The workbook and document adapt existing templates. Scope and contribution: local data-processing workflow, validation rules, testing, report preparation and handover documentation. No live API integration or production deployment is claimed. Available for clearly scoped, one-off data cleanup, Excel/CSV reconciliation and document-processing tasks with written briefs and asynchronous communication.
- Developer