> Markdown version of [/jobs/ext/2726578-analytics-engineer-product](https://www.wearedevelopers.com/jobs/ext/2726578-analytics-engineer-product). 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). --- # Analytics Engineer, Product - **Company:** Inato - **Location:** Paris, France (Remote available) - **Experience:** Expert - **Salary:** €65,000.0 - €80,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Data Analysis, BigQuery, SQL Databases, Data Streaming, Data Pipelines - **Published:** September 5, 2026 - **Apply:** https://startup.jobs/senior-analytics-engineer-product-inato-8144581 ## About the Role Must-have * 4+ years as an Analytics Engineer, or in a hybrid data engineer/analyst role with strong analytics engineering ownership. * Strong dbt + warehouse modeling skills (we use BigQuery). * Track record of owning data products end-to-end: definition * ship * measurable adoption. * A genuine product mindset - you start from the user's problem, not from the SQL. * Excellent written and verbal communication with non-data stakeholders. * Comfort navigating ambiguity - you can turn a vague operational pain into a working data asset without a fully-specified ticket. ## Description We're hiring a Senior Analytics Engineer, Product to own how data moves from Inato's platform into the hands of the people who depend on it - our product squads, CS and Marketing teams, sponsors, and sites. You'll enable trusted self-service at scale, prove the value of our new product offerings faster, and ship data products directly into the user experience. You'll report to Alexandre Halley (Data Director) and partner daily with Product squads (PMs, engineers, designers), with regular touchpoints into CS, Marketing, and Sponsor / Site Ops. Our stack includes Segment, Airbyte, Dagster, BigQuery, dbt, Hex, FullStory, and more. What you'll own * Trusted self-service at scale. Own metric definitions, build the semantic layer powering our AI-driven self-service in Hex, and govern what gets exposed so non-data teams can answer their own questions confidently. * Faster value validation for new offerings. Partner with squads to translate vague operational pains (a CS workflow, a sponsor enablement gap, a prototype idea) into working data assets in days - from real-data prototype to production-grade pipeline. * Data products in front of users. Own the tables consumed by the product itself, and the user-facing dashboards (embeds, custom interfaces) that sponsors, sites, and internal teams rely on day to day. * Using AI to scale yourself and the team. Automate the parts of data work that don't need a human in the loop so the team can keep pace as new offerings multiply and Inato grows. The data team is pioneering AI-enablement internally, and this role is at the front of that. What success looks like * By 3 months: You own at least one new-offering data pipeline end-to-end. Corresponding data points are available for self-service, and new metric definitions are live. You've automated at least one internal process using AI. * By 6 months: At least three user-facing dashboards or data streams are live. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Making Data Warehouses fast. 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