Redacted preview of the public procurement tooling

Private (redacted)

Public procurement tooling (redacted)

Private engagement: pipeline + app that turns tenders and awards into decision-ready data. Screens are redacted; no names or amounts.

Stack

Python · SQL · ETL · Analytics app

Artifacts

Redacted / NDA

Context

In procurement, information is scattered across sources with inconsistent schemas and fields that drift over time.

The goal: a coherent view for analysis, monitoring, and reporting, with auditability and traceability.

Note: under NDA, names, entities and amounts are omitted; visuals are redacted.

Decisions

  • Canonical analytics model (contracts, suppliers, items, timelines, awards).
  • Robust ingestion: validation, deduplication, and change tracking to handle schema drift.
  • Supplier/classification matching with deterministic rules + assisted review.
  • Action-oriented UI: essential filters + export for recurring reporting.

Architecture

Architecture diagram for the public procurement tooling
Sources → ingestion → normalization/matching → analytics warehouse → app + reports.
  • Logging and traceability to explain where each number comes from.
  • Clear separation: ingest/validate, transform, analytics model, UI/reporting.

Outcome

  • Lower friction for supplier, timeline, and award analysis.
  • Consistent entities and classifications over time.
  • Reusable base for audits, alerts, and recurring reporting.

Links