ការងារ
ពីប្រភពដៃគូ
Founding Analytics Engineer
តម្លៃតាមការស្នើសុំ
ព័ត៌មានលម្អិត
- ប្រភេទការងារ
- ពេញម៉ោង
- ពីចម្ងាយ
- ទេ
- ក្រុមហ៊ុន
- Zefir
ការពិពណ៌នា
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.
• 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 Zefir is not decided yet, and you are the one who decides it.
It suits a doer’s mindset, including someone who has already led and wants to go back to building.
Hiring process
• Screening with David, Talent (30 min)
• Deep dive with Gabriel, Engineering Lead: your past experience and what you want next (45 min)
• Technical interview with Diane, your future manager: data platform, modeling and tracking (1h)
• Cultural interview with Louis, co-founder, plus an informal exchange with a function lead (1h)
• Reference calls, then offer 🎉
Benefits
The following is for permanent employees only. For other contracts (interns, apprentices, fixed-term..), please, check with your recruiter.
• Competitive salary : You can run your own simulation with our salary calculator .
• BSPCE (Stock Options) : Available for everyone, with monthly vesting after year one, over a 4-year period.…
Source: Arbeitnow (https://www.arbeitnow.fr/jobs/companies/zefir/founding-analytics-engineer-paris-74468)
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.
• 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 Zefir is not decided yet, and you are the one who decides it.
It suits a doer’s mindset, including someone who has already led and wants to go back to building.
Hiring process
• Screening with David, Talent (30 min)
• Deep dive with Gabriel, Engineering Lead: your past experience and what you want next (45 min)
• Technical interview with Diane, your future manager: data platform, modeling and tracking (1h)
• Cultural interview with Louis, co-founder, plus an informal exchange with a function lead (1h)
• Reference calls, then offer 🎉
Benefits
The following is for permanent employees only. For other contracts (interns, apprentices, fixed-term..), please, check with your recruiter.
• Competitive salary : You can run your own simulation with our salary calculator .
• BSPCE (Stock Options) : Available for everyone, with monthly vesting after year one, over a 4-year period.…
Source: Arbeitnow (https://www.arbeitnow.fr/jobs/companies/zefir/founding-analytics-engineer-paris-74468)
បញ្ជីប្រកាសនេះមកពីប្រភពដៃគូ។ សូមដាក់ពាក្យនៅលើគេហទំព័រប្រភព។
ប្រភព: Zefir
ទីតាំង
Paris, បារាំង
បញ្ជីត្រូវបានផ្តល់ដោយ Zefir។
