The operating problem
Sales, stock, and incoming supply arrive through different systems and at different times. Reporting becomes fragile when every dashboard has to reconcile those differences independently.
What we built
We built ingestion notebooks and scheduled data workflows that separate raw source records, cleaned business entities, and reporting-ready outputs. Python and SQL handle parsing, transformations, and reconciliation in Databricks, with Delta tables providing a consistent analytical storage layer.
The work includes explicit schemas, source-specific checks, and reusable reporting contracts. Separate business datasets remain distinct, while downstream reports can consume defined measures instead of rebuilding the same logic.
What the work enables
The implemented foundation gives analytical consumers a traceable route from source feeds to business-ready data. It does not make every upstream system real-time; refresh timing and source completeness still matter.
A dependable dashboard starts with a data contract, not with a chart.