CDI - Manager Context & Analytics Engineer - Paris

Mission

An agent is only as good as the data and knowledge it relies on. Pipelines, data models, metric definitions, access rights: you build and govern this foundation, and our clients' applications and agents rely on it. When an analyst or agent requests a figure, the answer is from the finance team, accompanied by its definition and source.

This position is for you if you want business units and agents to be able to trust data and use it.

Artefact supports the world's largest companies in deploying AI: 3,000 AI makers in over 30 countries, over 1,000 clients. In France, the Engineering department brings together around 300 people under the CTO's responsibility; you will join the AI Applied Engineer chapter, the Context & Analytics Engineer archetype.

Activities and Responsibilities

With business teams and analysts, the goal is to define what each metric means and verify that the data corresponds to it. On this foundation, Product Engineers and AI Engineers build applications and agents.

Platform Engineers operate the infrastructure on which your pipelines run. Interactions with client data owners and governance teams, peer code reviews, and onboarding new recruits complete the day-to-day.

Your Responsibilities:

• Build ingestion pipelines and data models, using dbt for example.
• Implement data quality controls and maintain lineage.
• Build the semantic layer and metric definitions, so that people and agents use the same figures, including in text-to-SQL.
• Define governance rules and access rights.
• Build knowledge pipelines for RAG: indexing, chunking, metadata, permissions.
• Prepare context usable by agents: metadata, definitions, lineage, and permissions.

Profile Sought

The Essentials:

1. You write SQL with ease and model data for analysis.
2. You have a track record of scheduled, tested, and monitored data pipelines.
3. Alongside business teams, you define metrics and verify that the data truly means what everyone believes it reads.
4. Data quality, documentation, and access rights matter to you.
5. Coding agents are already part of your daily work, and you verify their output.

A Plus, Not a Prerequisite:

• Python for data processing.
• A cloud data platform, such as BigQuery, Snowflake, or Databricks.
• A semantic layer or a metrics store.
• Preparation of documents for RAG: chunking, indexing, metadata.
• Data catalog or lineage tools.

You don't need to tick all the boxes to apply: if you have the essential requirements and the desire to learn the rest, let's talk.

Why Join Us?

In a field where two dashboards sometimes show two different figures, this role is about restoring trust: a correct figure, a shared definition, an identifiable source. This work, discreet when successful, underpins everything else that other archetypes build on top.

We are AI makers: AI is built with our clients, not just for them. Their challenges become ours, and our technical choices are explained in business language. Our positions are clear; they evolve in the face of facts, never under pressure.

Our domain changes every month, and we are comfortable with this pace: new technologies and new sectors are learned quickly, and habits that are no longer useful are abandoned without regret. Faced with a difficult problem, the reflex is to break it down into verifiable steps, with a high level of requirement for both code and deliverables.

When something is missing, for a client or for the chapter, we build it. Progress comes through constructive feedback, knowledge sharing, asking for help without delay, and recognizing the work of others. There is much to learn, and much to build.

A counselor will support you in your first months. Technical onboarding opens the journey, followed by a first client project; if none are ready, internal starter kits take over.

The rest is learned on projects, with AI Engineers and the support of the Context & Analytics Engineer community, particularly context engineering, which is the work that makes data usable by AI agents. From 2027, the Context & Analytics Engineer archetype will have its own skills matrix and training path, in addition to the common foundation for all AI Applied Engineers (AI evaluation, production responsibility).

Later, a transverse role becomes possible (Project Tech Lead, Pre-sales Engineer, Engineering Manager), as do the AI Architect and AI Product Manager chapters.

Recruitment Process

The process takes approximately four hours, divided into six steps, with only one on-site.

1. HR Interview (45 min, remote): background, motivations, and practical points, such as timeline and salary expectations.
2. Call with the CTO (15 to 30 min, remote).
3. Technical Screening (30 min, remote, with a member of the AI Applied Engineer chapter): detailed review of your past projects, followed by technical and solution design questions.
4. Code Review and Live Coding (60 min, on-site, with a chapter member): review of an existing code repository and correction of its flaws, followed by building a new feature with a coding agent and presenting the result.
5. Technical Case Study (45 min, remote, with a chapter member): framing a business problem, proposing a technical solution, describing the evaluation method, and then discussing your choices.
6. Final Interview (30 min, remote, with a member of the department's management, Engineering Manager or Director): review of any outstanding points, selection of the archetype and level best suited to your profile, and answers to all your questions.

Depending on the steps, all AI tools are allowed, or none. During live coding, the way of working with AI assistance is observed.

Compensation: €66,000 to €75,000 gross annual salary (fixed) + up to €10,000 variable depending on experience and seniority

At Artefact, we are convinced that diversity of backgrounds, experiences, and perspectives enriches our teams. We are committed to providing an inclusive environment and equitable opportunities, where everyone can grow and fully express their potential. Our recruitments are based on skills, potential, and fit for the role.