The business question
The orbital launch market is scaling fast, but the public conversation runs on headlines rather than metrics. AZIMUTH treats it like any other market a BI team would cover: who is launching, how often, how reliably, with how much reuse, and how much mass is actually reaching orbit — trends an analyst, investor, or space enthusiast can interrogate instead of guess at.
Lakehouse architecture
The platform follows a medallion architecture on Databricks: raw launch data lands in bronze, gets cleaned and conformed in silver, and is modeled into an analytics-ready star schema in gold with dbt, where data-quality tests guard every layer boundary.
The star schema
The gold layer is dimensionally modeled around a single fact table, fct_launches, with one row per launch attempt. Every question the platform answers — cadence per provider, reliability by vehicle, reuse rates over time, mass to orbit by destination — resolves to measures on the fact grain sliced by conformed dimensions.
What it answers
- Cadence: launches per provider per quarter, and how the market's tempo is shifting.
- Reliability: success rates by vehicle and vehicle family over time.
- Reuse: what share of launches fly on previously flown boosters, and how that trends.
- Mass to orbit: the metric that matters most — how much payload actually reaches each orbit class, by whom.
The platform is in active development; the repository tracks progress as the models and dashboard take shape.
Seeking Data Analyst & BI Analyst roles
I'm actively exploring opportunities in Seattle or remote. If your team builds with the modern data stack, I'd love to talk.