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# AI Data Enablement Engineer

- **Company:** LEOVEGAS MOBILE GAMING GROUP
- **Location:** Barcelona, Spain (Remote available)
- **Salary:** €42,000.0 - €78,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, ARM Architecture, Computer Vision, Clinical Data Repository, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Python (Programming Language), Meta-Data Management, Query Optimization, Role-Based Access Control, Cloud Services, Salesforce.Com, SAP (Applications), Microsoft SharePoint, SQL Databases, Unstructured Data, Large Language Models, Snowflake, Multi-Agent Systems, Caching, Data Layers, Build Management, Pyspark, Pure Data, Streamlit Framework, Data Pipelines, Databricks
- **Published:** September 18, 2026
- **Apply:** https://www.adzuna.es/contact-us.html

## About the Role

Must-Have Experience

 + 5+ years hands-on data engineering on cloud data platforms - Snowflake and/or Databricks demonstrated in real project delivery, not skill-list-only
 + Direct hands-on experience with either Snowflake Cortex or Databricks Genie - you have built, configured, and tuned these in production or advanced pilots, with specific reference to the flavors used (Cortex Analyst / Search / Agents / LLM Functions, or 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, Snowpark, 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
 + SnowPro Advanced or 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

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 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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