What Data & AI roles do you recruit for?

We place professionals across the full data and AI landscape, from individual contributors to leadership.

On the engineering side: Data Engineers, Analytics Engineers, Data Platform Architects, and MLOps Engineers, working across Python, Spark, Databricks, Snowflake, dbt, Kafka, and the major cloud platforms (Azure, AWS, GCP).

On the science and AI side: Data Scientists, Machine Learning Engineers, and AI Engineers, including specialists in large language model applications, RAG architectures, and production AI systems.

For analytics: BI Developers, Analytics Translators, and Data Analysts working across Power BI, Tableau, Looker, and SQL-based environments.

At leadership level: Heads of Data, Chief Data Officers, and technical leads for data platform and AI product teams.

We also place Data Governance and Data Management specialists, an area of growing demand as organisations mature their data practices under GDPR and internal compliance requirements.

Do you place Data & AI contractors as well as permanent hires?

Yes. We handle both permanent appointments and contract engagements. Demand for experienced data engineers and AI specialists on a contract basis has grown significantly, particularly for organisations building or migrating data platforms. Most roles in this space are filled through direct approach rather than open postings.

How do you find Data & AI candidates who are not actively job-hunting?

The strongest engineers, scientists, and technical leads in this space are rarely on the open market. Our consultants maintain direct relationships built through ongoing conversations, not reactive searches triggered by a live brief. When a role comes in, we are already talking to the right people. For candidates, this means access to opportunities that are never publicly advertised.

What skills are clients in Germany currently asking for in Data & AI?

Cloud platform experience is consistently requested, with Azure dominant in the German enterprise market. Databricks and Snowflake have become near-standard requirements for data platform roles. Python is assumed for anything data science or AI-related. For AI specifically, clients are moving beyond proof-of-concept and want engineers who have taken models into production, which means MLOps and AI engineering skills are in higher demand than pure research backgrounds. Data governance and quality tooling, including dbt, is increasingly requested even in roles not formally titled around governance. SQL remains foundational across virtually every role.