About the role
AI is only as reliable as the definitions it relies on. In most companies, revenue, margin or an active customer have three different definitions depending on the department. When that happens, no dashboard and no AI agent will give the right answer. At Diametral, the Analytics Engineer builds the layer that turns raw data into a documented, tested source of truth that teams and AI can query.
You join the Decision Analytics engine, working between Data Engineers and Data Analysts.
What you will build
- Robust, versioned analytical data models (dbt, SQLMesh), from raw data all the way to business data marts.
- Semantic layers and metric repositories (dbt Semantic Layer, Cube, Power BI semantic models, LookML): one single definition for every KPI.
- AI-ready data foundations that let conversational agents and text-to-SQL tools give the right answer.
- Software engineering practices applied to analytics: Git, CI/CD, automated testing, data contracts, observability.
- Documentation and lineage that make data readable, even for people who are not data specialists.
- Migration and rationalization of legacy BI estates to modern stacks.
Your tech stack
SQL, dbt, SQLMesh, Snowflake, BigQuery, Databricks, Microsoft Fabric, Airflow, Dagster, Cube, Elementary, Soda, Great Expectations, Power BI, Looker, Tableau.
Within Diametral
- You contribute to our accelerators: dbt templates, test packages, modeling standards.
- You bridge the Architecture & Engineering and Applied AI & Data Science engines.
What we are looking for
What you bring
- An engineering degree or master's in computer science, data or statistics.
- At least 3 years of experience in analytics engineering, BI engineering or data engineering, excluding internships and apprenticeships.
- Excellent command of SQL and data modeling (dimensional, Data Vault, One Big Table).
- Advanced, production-grade practice of dbt or an equivalent tool.
- Fluent English in a client setting, plus the language of your home office.
What will make the difference
- Experience implementing a semantic layer at enterprise scale.
- A dbt, Snowflake, Databricks or Fabric certification.
- Experience with conversational AI projects on structured data.
Your mindset
- High standards: a metric without tests is a metric no one can trust.
- A taste for clean, reusable engineering.
- The ability to align business and IT on a single definition.
What we expect from all our talent
- An obsession with business impact: you quickly understand our clients' business, and you know how to challenge it.
- A strong sense of service and client experience.
- Top-level communication, written and spoken: you simplify, you bring people along.
- Advanced mastery of AI and automation tools: you can show us an agent or a prototype you built yourself.
- Ambition and rigor, serving a team that puts the collective before the individual.
Interview process
- Interview with the Talent Acquisition team and/or a Business Manager
- Technical interview with an engine expert
- Interview with the Managing Director or the Engine Director
Apply
Choose your home office and share your profile. Our Talent Acquisition team will get back to you within a few days.
