Founding Analytics Engineer

Zefir; Paris; Sur place; EUR 90 000 - 130 000; Plein temps

Résumé du poste

Zefir in Paris is seeking a Senior Data / Analytics Platform Engineer to own the canonical data layer and semantic model. You will define metrics, governance, and secure, self-serve analytics for Growth, Finance, Ops and Product.

You\'ll work with BigQuery, dbt, Python and orchestration tools to standardize KPI definitions and data access. Fluency in English and French is expected, and autonomy is valued in a founders-led environment.

Qualifications

• 7+ years in Data/Analytics/Platform Engineering or equivalent.
• Experience shipping end-to-end data platforms and semantic layers.

Responsabilités

• Own canonical data models and metric definitions.
• Define a robust semantic layer with clear contracts between data producers and consumers.
• Enable self-serve analytics with row- and column-level security, governance and documentation.
• Collaborate with Growth, Finance, Ops and Product to align KPI definitions and trust in data.

Connaissances

BigQuery / Snowflake

SQL

Python

Data modeling

Orchestration tools

Analytics governance

Outils

dbt

Airflow

Dagster

Prefect

Fivetran

Airbyte

Segment

Description du poste

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 escalates, 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.

Comfortable with ingestion patterns (Fivetran, Airbyte, CDC), reverse-ETL (Hightouch, Census, Segment), and access governance (IAM, row- and column-level security, PII tagging).

You have built and owned a semantic or metrics layer, and you can arbitrate metric definitions with Finance, Ops and Growth without flinching.

You treat AI agents as first-class data consumers: exposing data through MCPs, semantic APIs or text-to-SQL, with proper guardrails.

Strong ownership: you write the standards, defend them, and fix what is broken without waiting for permission.

A clear communicator who turns "ping the data person" rituals into self-serve handoffs.

Fluent in English and French.

The honest trade-off

There is no data team to manage, and none planned in the short term. You get real autonomy and a direct line to the founders and function leads, in exchange for building alone before maybe building a team. At a comparable proptech you would join an existing team and an existing roadmap. Here, what a metric means at Ze