Founding Analytics Engineer

Zefir·Parissenior

In short

→Ingeniero de Analytics fundador responsable de construir la capa de datos confiable y escalable para un agente AI de venta de propiedades en Europa.
→Día a día: crear modelos unificados, definir métricas canónicas, establecer gobernanza de datos y habilitar a equipos a través de un layer semántico seguro y au
→Lo destacado: eres el primer hire dedicado a datos; tu trabajo define cómo todo el equipo entiende y utiliza los datos, con impacto directo en decisiones de inv

No se requiere inglés, pero se trabaja en un entorno global con documentación en inglés.

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What they ask for

✓7+ años como ingeniero de datos, analytics o plataforma, idealmente en empresa de mercado o consumo rápido.
✓Experiencia comprobada con eventos en producción: taxonomía, tracking cliente/servidor, atribución y cumplimiento GDPR.
✓Habilidades sólidas en BigQuery, dbt, SQL avanzado, Python y orquestación (Airflow, Dagster o Prefect).
✓Experiencia con herramientas de ingestión: Fivetran, Airbyte o CDC.
✓Habilidades para establecer gobernanza de datos: modelos canónicos, contratos entre equipos, definiciones unificadas de métricas.
✓Capacidad de construir y mantener un layer semántico escalable con seguridad a nivel fila y columna.

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BigQuerydbtSQLPythonAirflowDagsterPrefectFivetranAirbyteCDC

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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 over $55 million from top-tier investors like Sequoia Capital, 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. What success looks like after 12 months One documented event taxonomy, actually used by Engineering, Growth and CRM. One semantic layer where every shared KPI has a single definition, a single owner and a version history. New joiners understand the data model in days, not months. Published freshness and failure SLAs, an explicit ingestion topology, and no production path depending on BI tables. Ops, Growth, Finance and AMs build most of their recurring dashboards themselves, and AI agents query the data layer safely through curated MCPs. Growth attribution is trustworthy enough that annual acquisition spend decisions are defensible end to end. What we are looking for 7+ years as a Data, Analytics or Platform Engineer, ideally including a stint at a fast-moving consumer or marketplace company. Staff or Lead exposure expected. Hands-on with the modern data stack: BigQuery (or Snowflake, Redshift), dbt or equivalent, advanced SQL and data modeling, Python for pipelines, orchestration (Airflow, Dagster, Prefect). You have shipped event tracking and instrumentation in production, end to end: taxonomy, client and server-side events, attribution, GDPR-compliant opt-out, propagation downstream.

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