Our Client's Digital Finance IT is building an AI-enablement layer on top of our enterprise data platform to enable business users across Finance to interact with governed data in natural language. We're hiring a Data Enablement Engineer to design, build, and operate the trusted datasets, semantic models, and embedded AI experiences that make this possible. This is a data platform engineering role, not a data science or model-building role. You will spend your time engineering the data foundation that makes AI reliable — semantic layers, governed data products, and embedded natural-language analytics — not training models.
What You'll DoDesign and build AI-ready data products on Databricks — trusted datasets with well-defined business semantics, KPIs, hierarchies, and business glossary alignmentImplement semantic layers and governed datasets that support both traditional BI consumption and natural-language querying by business usersDeploy and operate Databricks Genie spaces with Unity Catalog, tuning them for accuracy, adoption,



and business relevanceBuild RAG pipelines and conversational analytics applications grounded in governed enterprise data — including Streamlit or Databricks Apps that let business users query data without writing SQLEngineer robust ETL/ELT pipelines (dbt, Airflow, PySpark) that produce and maintain the trusted data these AI experiences depend onImplement data governance — RBAC, row/column-level security, masking, lineage, auditability, catalog and metadata management — in a regulated pharma environmentOptimize cost and performance on both the data platform side (warehouse sizing, cluster tuning, query optimization) and the AI side (token usage, caching, model routing)Partner with Finance business stakeholders to translate domain requirements into semantic models and governed data products they can trustRequirementsMust-Have Experience5+ years hands-on data engineering on cloud data platforms — Databricks demonstrated in real project delivery

📌 AI Data Enablement Engineer (Madrid)
🏢 Xenon Seven
📍 Madrid