Data Engineering Beyond "Pretty Charts": How I Built an Aviation Telemetry Leakage Engine

Question

Stop Tracking "On-Time" Flights. Track Tarmac Fuel Destruction Instead.

Most aviation dashboards track gate-to-gate "On-Time" arrivals. The problem? It's a lagging indicator that completely masks why airlines silently hemorrhage millions. Padded flight schedules disguise a massive bottleneck: chronic tarmac fuel destruction occurring right before takeoff.

I built AeroOps, an end-to-end telemetry and multi-domain operations leakage engine, to expose exactly where systems drop value between the hangar and the runway.

• Repo & Technical Report: [GitHub Link]
• Stack: MySQL (Star Schema & CTEs) · Telemetry Data Modeling · Executive Dashboard Architecture · Power BI (DAX)

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The Architectural & Engineering Challenge

Handling flight telemetry means dealing with high-volume, disjointed datasets. To make this actionable, the engineering required:

1. Star Schema Data Warehousing: Joined 513,000+ flight legs with precise technical burn parameters pulled directly from the ICAO Aircraft Emissions Databank.
2. Bypassing Relational Boundaries: Formulated deduplicated DAX lookup models to handle wide-body propulsion penalties under sub-second memory constraints.
3. Algorithmic Baseline Filters: Enforced a strict 4-minute engine warm-up buffer directly into the SQL logic. This stripped away safe, expected operational windows to isolate true, excess ground latency.

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The Bottom-Line Impact Metrics

By querying raw transaction logs and user event data instead of building passive, "pretty" charts, the engine surfaced massive operational friction:

• $42.69M in Tarmac Cash Destruction: Isolated and quantified the exact cost of ground traffic gridlocks at major hubs like Atlanta (ATL).
• 54% to 57% Short-Haul Menu Spoilage: Exposed a massive waste loop across major carriers (Delta/American) due to over-boarding. Transitioning the system to a 100% shelf-stable inventory model reclaimed $2.58M in simulated margin leaks.
• $725K Legal Fine Exposure: Isolated a hidden 9% telemetry sync drop rate specifically among regional commuter airframes caused by corrupted logs.
• Prevention of $1.50M Asset Groundings (AOG): Coded an automated Reorder Point (ROP) trigger tool to insulate warehouses against catastrophic out-of-stock asset scenarios.

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The Takeaway

Data shouldn't just look nice in a presentation deck. If your queries aren't actively protecting business margins or exposing systemic infrastructure leaks, you're looking at the wrong metrics.

If your team is scaling fast and dealing with messy relational pipelines or unoptimized telemetry data, let's connect and fix your backend infrastructure loops.