06 ott - Italia
Xenon Seven
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.
Aumenti le sue possibilità di arrivare alla fase del colloquio leggendo la descrizione completa del lavoro e candidandosi tempestivamente.
What You’ll Do
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- Design and build AI-ready data products on 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 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, 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 da
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