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 Do
Design and build
AI-ready data products
on Snowflake and/or Databricks — trusted datasets with well-defined business semantics, KPIs, hierarchies, and business glossary alignment
Implement
semantic layers
and governed datasets that support both traditional BI consumption and natural-language querying by business users
Deploy and operate
Snowflake Cortex
capabilities (Cortex Analyst, Cortex Search, Cortex Agents,
Cortex LLM Functions) and/or
Databricks Genie spaces
with Unity Catalog, tuning them for accuracy, adoption, and business relevance
Build
RAG pipelines and conversational analytics applications
grounded in governed enterprise data — including Streamlit or Databricks Apps that let business users query data without writing SQL
Engineer robust ETL/ELT pipelines (dbt, Airflow, Snowpark, PySpark) that produce and maintain the trusted data these AI experiences depend on
Implement
data governance
— RBAC, row/column-level security, masking, lineage, auditability, catalog and metadata management — in a regulated pharma environment
Optimize
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
📌 AI Data Enablement Engineer (Madrid)
🏢 Xenon7
📍 Madrid