David Wolf · Portfolio Use Case
A travel-data quality project cleaning geographic waypoints, inventory metadata, and GDS-linked destination structures to improve search, booking, routing, and content reliability.
Contributed to geographic waypoint, destination inventory, and GDS cleanup work in a travel-technology environment, improving the structure, accuracy, and usefulness of location-linked inventory data used across search, itinerary construction, affiliate traffic, and booking workflows.
Client
Cendant / Orbitz / GTA Gullivers Travel Associates
Engagement Type
Early-career role; exact employment or consulting classification requires confirmation
Period
Early Career; exact dates require confirmation
Role
Travel Technology / Data Quality / Technical Marketing Contributor; exact title requires confirmation
Focus Areas
Geographic Waypoint Cleanup, GDS Inventory Cleanup, Destination Metadata
The Context
Online travel platforms depend on geography. Search, routing, hotel inventory, affiliate pages, landing pages, and booking flows all assume that cities, airports, regions, hotels, and waypoints are correctly represented.
The Challenge
Travel data is inherently messy. Locations have aliases, duplicates, nearby airports, supplier-specific names, overlapping regions, and route-dependent meaning. Cleanup work had to improve usefulness for search, booking, and technical marketing, not just make records look cleaner.
What I Did
The Outcome
The project became an early foundation for later work in schema normalization, entity resolution, AI data pipelines, and security analytics. It showed the same pattern at a smaller scale: normalize messy real-world data so systems can reason over it reliably.
Geographic
Waypoint, destination inventory, and GDS-linked data cleanup in a travel-technology environment
Improved
Metadata quality for search, itinerary, routing, affiliate, and booking use cases
Key Deliverables
Collaboration
Worked in a travel-technology context where data quality, supplier inventory, geographic taxonomy, search relevance, technical marketing, and booking workflows intersected. Exact team structure and stakeholders should be refined after source confirmation.
Client
Cendant / Orbitz / GTA Gullivers Travel Associates
Engagement Type
Early-career role; exact employment or consulting classification requires confirmation
Period
Early Career; exact dates require confirmation
Role
Travel Technology / Data Quality / Technical Marketing Contributor; exact title requires confirmation
Focus Areas
Geographic Waypoint Cleanup, GDS Inventory Cleanup, Destination Metadata
The Context
Online travel platforms depend on geography. Search, routing, hotel inventory, affiliate pages, landing pages, and booking flows all assume that cities, airports, regions, hotels, and waypoints are correctly represented.
The Challenge
Travel data is inherently messy. Locations have aliases, duplicates, nearby airports, supplier-specific names, overlapping regions, and route-dependent meaning. Cleanup work had to improve usefulness for search, booking, and technical marketing, not just make records look cleaner.
What I Did
The Outcome
The project became an early foundation for later work in schema normalization, entity resolution, AI data pipelines, and security analytics. It showed the same pattern at a smaller scale: normalize messy real-world data so systems can reason over it reliably.
Geographic
Waypoint, destination inventory, and GDS-linked data cleanup in a travel-technology environment
Improved
Metadata quality for search, itinerary, routing, affiliate, and booking use cases
Key Deliverables
Collaboration
Worked in a travel-technology context where data quality, supplier inventory, geographic taxonomy, search relevance, technical marketing, and booking workflows intersected. Exact team structure and stakeholders should be refined after source confirmation.
At a Glance
Focus Areas
Tools & Technologies
Evidence & Artifacts
Public-Safe Caveat
This case study is based on user-provided project context and should be treated as a draft scaffold until exact company entity, role title, dates, systems, record counts, and measurable outcomes are confirmed from resume, LinkedIn/Profile, archived work samples, or other records.
David Wolf
AI Security · Product Security · Security Leadership
Based on analyzed public signals, not proof of any individual's or company's internal state.
