Data Engineer

Without reliable data, there is no AI in production. At Diametral, the Data Engineer builds the foundations on which our clients create their AI-native systems: modern, governed data platforms designed to power analytics, machine learning and generative AI at scale.

You join the Architecture & Engineering engine and work on high-stakes programs for major companies in industry, energy, retail, insurance and luxury, across Europe and the Americas.

What you will build

• Next-generation cloud data platforms: lakehouses on Databricks, Snowflake, Microsoft Fabric or BigQuery, on Azure, GCP or AWS.
• Industrialized ingestion and transformation pipelines, batch and streaming, robust, idempotent and observable.
• Clean, documented analytical data models with dbt, serving business teams and data products (Analytics Engineering).
• The foundations that feed AI use cases: data prepared for RAG, feature stores, unstructured data.
• End-to-end automation: Airflow orchestration, CI/CD, infrastructure as code (Terraform), monitoring and FinOps.
• Migrations of legacy platforms (Hadoop, SAP BW, on-premise warehouses) to the cloud.

Your tech stack

Python, SQL, Spark, Databricks, Snowflake, Fabric, dbt, Airflow, Kafka, Terraform, Docker, GitLab CI or GitHub Actions, Azure, GCP, AWS.

Within Diametral

• You contribute to our engineering standards and to accelerators reused from one client to the next.
• You share your tech watch within the engine community and mentor more junior profiles.

What you bring

• An engineering degree or a master's in computer science or data.
• At least 3 years of experience in Data Engineering or Analytics Engineering, excluding internships and work-study.
• Excellent command of Python and SQL, and of at least one public cloud.
• Hands-on experience with a modern platform: Databricks, Snowflake, Fabric or BigQuery.
• A real DevOps culture: Git, CI/CD, containers, infrastructure as code.
• Fluent English in a client context, plus the language of your home office.

What will make the difference

• A Databricks, Snowflake or cloud certification.
• First experience preparing data for GenAI use cases.
• A taste for architecture: modeling, technology choices, documentation.

Your mindset

• Demanding on quality, pragmatic in your choices.
• Able to explain a technical trade-off to a business stakeholder.
• Team-oriented, curious, generous in sharing.

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 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