[Hiring] Founding Analytics Engineer @Zefir

Role Description

Zefir is building an AI autopilot for home sales in Europe, starting in France: an AI agent runs the entire sale and purchase journey end-to-end, orchestrating local brokers, portals, buyers, and documents. Backed by over $55 million from top-tier investors including Sequoia Capital, an investor in OpenAI and the lead backer of Pennylane’s early rounds.

An AI agent that runs a property transaction end to end only works if the data underneath it is reliable, governed and cheap to query. Right now it is none of those three. That is the job.

This is the first dedicated data hire in years. The foundations run, nobody owns them yet.

• Ingestion and centralisation into BigQuery already work, so you are not starting from an empty warehouse.
• What is missing is everything downstream:
  • a canonical structure
  • one definition per metric
  • query cost under control
  • a self-serve layer so that Finance, Growth and Ops stop routing every question through one person
• Today the stack holds because individuals across Ops, Growth, Finance and Engineering compensate locally.
• It works, and it is fragile:
  • KPIs drift between tools
  • tracking breaks silently
  • costs escalate
  • nobody owns the translation between raw engineering data and decision-ready truth
• You will be the single accountable owner of that layer. Not a support function, not a ticketing desk, not a BI factory.

What you will own

• Canonical models and metric definitions:
  • a documented semantic layer with canonical entities (Buyer, Seller, Asset, Agent)
  • Bronze / Silver / Gold layers
  • Clear contracts between what Engineering exposes and what BI consumes, so that KPI debates stop being about whose number is right
• Self-serve enablement:
  • clean models
  • consistent BI primitives
  • row- and column-level security, so Ops, Growth, Finance and Account Managers build their own dashboards without compromising compliance
• Analytics and tracking governance:
  • the global event taxonomy and tracking roadmap
  • a hybrid client-side and server-side event strategy
  • consistent sync across CRMs and marketing platforms
  • GDPR consent flows by design
• Platform reliability, safety and cost:
  • tested and versioned transformations
  • monitoring of freshness, failures and usage
  • sane ingestion patterns
  • no production code path depending on BI tables

One thing worth stating plainly: our AI tooling already queries the warehouse directly, and the cost of it is not under control yet. Designing the guardrails, the schema curation and the authorisation layer is part of the job from week one, not a phase two.