Stefan R.

OTA & Airline Pricing Data Analyst | Validation, QA & Python Pipelines

I find the pricing and logic errors that quietly cost travel companies revenue — before they reach customers or decision systems. As the sole data-validation analyst on a travel loyalty-optimisation platform, I've independently QA'd a loyalty-and-pricing engine with 100,000+ rules, validate hundreds of fares daily, and build the calculation models that confirm an app's output matches real-world earning and pricing behaviour. WHO I HELP Travel-tech teams, loyalty platforms, OTAs and airline-programme tools that depend on large, fast-moving pricing and earning data they need to trust. WHAT I DO - Competitor price monitoring across multiple travel sites - Fare and award anomaly detection (Deal / Flash Deal / Phantom Fare classification) - Independent calculation models that verify airline status, earning and pricing logic - Large-dataset verification, QA workflows and continuous data-consistency testing - Python data pipelines: API ingestion → PostgreSQL → analytics → dashboard HOW I BUILD IT I design end-to-end pipelines that pull live fares from the Amadeus and Duffel APIs, store append-only price observations in PostgreSQL, classify deals against rolling baselines, and gate every alert through a QA verification step before export. The result: clean, trustworthy data instead of false signals. TOOLS Python (pandas, SQLAlchemy, pipelines, automation) · PostgreSQL · SQL · Google Sheets · Excel · Amadeus & Duffel APIs · Streamlit · Docker · ETL A recent client put it simply: "Stefan helped us set up and test our complex logic system with over 100,000 rules — no small task." If you have pricing or loyalty data you can't fully trust, tell me what's breaking and I'll tell you how I'd validate it.