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# Founding Analytics Engineer

- **Company:** Zefir
- **Location:** Paris, France (Remote available)
- **Experience:** Expert
- **Contract:** Temporary 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), SQL Databases, Sql Optimization, System Availability, Snowflake, Production Code
- **Published:** September 5, 2026
- **Apply:** https://startup.jobs/founding-analytics-engineer-zefir-fr-8960880

## 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.
 * A clear communicator who turns "ping the data person" rituals into self-serve handoffs.
 * Fluent in English and French.

## Description

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. 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 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, 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.

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.


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