Who We Are

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 €60 million from top-tier investors Sequoia Capital and Kima Ventures, we're committed to accelerating life changes for millions of current and future European homeowners.

An AI agent that runs a property transaction end to end only works if the data underneath it is available, reliable and governed. That is the job.

Why this role exists

This is the first dedicated data hire in years. The foundations run, the steering is up to you.

While BigQuery already houses most of our data, we still lack key Growth data and a cohesive data governance framework. We need unified definitions, a canonical schema, and a robust semantic layer, enabling Growth, Finance, Ops and Product to self-serve insights efficiently and reliably.

Today the stack holds because individuals across Ops, Growth, Finance and Engineering compensate locally. They learned the quirks and built workarounds. It works, but it is fragile: KPIs drift between tools, tracking breaks silently, costs escalate, and 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) and Bronze / Silver / Gold layers. Clear contracts between what Engineering exposes and what each function consumes, so that KPI debates are aligned on the same metric.

Self-serve enablement. The submerged part of the iceberg: clean models, consistent BI primitives, row- and column-level security, so Ops, Growth, Finance and Account Managers build their own dashboards without compromising on accuracy.

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, and GDPR consent flows by design, so acquisition spend runs on attribution we can trust.

Platform reliability, safety and cost. Standards set once rather than team by team: tested and versioned transformations, monitoring of freshness, failures and usage, sane ingestion patterns (read replicas, CDC, batch), and no production code path depending on BI tables.

Data and AI driving decisions. Our internal AI tooling already queries the data warehouse for analyses. What’s missing is the core foundation: standardized metric definitions, reusable logic, and pre-computed data models.