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# AI Data Enablement Engineer

- **Company:** Xenon7
- **Location:** Spain
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, ARM Architecture, Computer Vision, Audit Trail, Clinical Data Repository, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Warehousing, Python (Programming Language), Meta-Data Management, Query Optimization, Role-Based Access Control, Cloud Services, Standard Sql, Salesforce.Com, SAP (Applications), Microsoft SharePoint, SQL Databases, Unstructured Data, Large Language Models, Multi-Agent Systems, Caching, Data Layers, Build Management, Pyspark, Pure Data, Streamlit Framework, Data Pipelines, Databricks
- **Published:** September 28, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=226d2621c86839c2

## About the Role

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

 * 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

## Description

* 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, * 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


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