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

-

- 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 data products they can trust

Must-Have Experience

-

- 5+ years hands-on data engineering on cloud data platforms — Databricks demonstrated in real project delivery, not skill-list-only

- Direct hands-on experience with Databricks Genie — you have built, configured, and tuned these in production or advanced pilots, with specific reference to the flavors used (Genie spaces with semantic models)

- Semantic layer / trusted data product delivery — you have built governed datasets that business users can rely on, with KPI definitions, hierarchies, and business glossary alignment

- dbt, PySpark, SQL, Python — strong across the modern data stack

- Orchestration with Airflow, Databricks Workflows, or equivalent

- Data governance in regulated environments — RBAC, RLS, masking, lineage, auditability

- Experience integrating structured and unstructured data (PDFs, SharePoint/Teams content, enterprise knowledge sources) into AI-enablement workflows

Nice to Have

-

xjncmbx
- Pharma, life sciences, or regulated financial services domain experience

- Veeva CRM, IQVIA, SAP, or clinical data source integration

- Streamlit or Databricks Apps for business-facing analytics

- Databricks Data Engineer Professional certification

- LangChain, LlamaIndex, or equivalent RAG frameworks

- Cost optimization on both compute (warehouse/cluster) and LLM (tokens/caching/routing) dimensions

What We’re NOT Looking For

-

- Data Scientists — this role is not model training, fine-tuning, LoRA/RLHF, or ML research

- Pure Data Engineers who list Cortex or Genie as a skill but haven't shipped it in production

- AI/GenAI engineers whose center of gravity is LangChain agents or RAG-over-documents, without a strong governed data platform foundation

- Computer vision, NLP model builders, or multi-agent orchestration specialists — wrong shape for this role

#J-18808-Ljbffr

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