Public procurement tooling (redacted)

• Client and contract details are under NDA (no names, amounts, or identifiers).
• Screens are redacted and show structure only (not operational data).
• I can discuss architecture, data model, validation, and workflow.

• Private pipeline + analytics app for procurement tenders and awards (redacted).
• Solved inconsistent schemas and drift across sources with validation, deduping, and traceability.
• Built a canonical analytics model + matching workflow and shipped an action-oriented UI with exports.

• Canonical analytics model that unifies messy source schemas into stable entities.
• Ingestion hardening: validation, deduplication, and change tracking for schema drift.
• Traceability-first logging so every number can be explained back to source.
• Deterministic matching rules + assisted review for entity/classification resolution.
• UI patterns for ops: essential filters, exports, and repeatable reporting.

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.

• 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.

• Logging and traceability to explain where each number comes from.
• Clear separation: ingest/validate, transform, analytics model, UI/reporting.

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

• Redacted UI preview