> Markdown version of [/jobs/ext/4004283-founding-analytics-engineer](https://www.wearedevelopers.com/jobs/ext/4004283-founding-analytics-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Founding Analytics Engineer - **Company:** Zefir - **Location:** Paris, France - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Data Analysis, BigQuery, Extract Transform Load (ETL), Data Warehousing, Identity and Access Management, Python (Programming Language), OpenAI, SQL Databases, Sql Optimization, System Availability, Snowflake, Agentic-AI, Production Code, Virtual Agents - **Published:** October 29, 2026 - **Apply:** https://www.welcometothejungle.com/en/companies/zefir/jobs/staff-analytics-engineer-data-platform_paris ## About the Role 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. Fluent in English and French. ## 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 €60 million from top-tier investors including Kima Ventures and 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. Why this role exists 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, and 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. They learned the quirks and built workarounds. It works, and 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 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, and GDPR consent flows by design. Platform reliability, safety and cost: tested and versioned transformations, monitoring of freshness, failures and usage, sane ingestion patterns, and 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.