AI Data Enablement Engineer

Xenon Seven; España; Presencial; EUR 70.000 - 110.000; Jornada completa

Descripción de la vacante

Xenon Seven seeks a Data Enablement Engineer to design, build, and operate trusted datasets, semantic models, and embedded AI experiences on our data platform. This role focuses on data foundation engineering, not model training.

You will work on semantic layers, governed data products, and AI-ready pipelines, collaborating with Finance to translate domain needs into reliable data products for business users.

Formación

• 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 — built, configured, and tuned in production or advanced pilots, with Genie flavors
• Semantic layer / trusted data product delivery — governed datasets 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 into AI-enablement workflows

Responsabilidades

• 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

Conocimientos

Databricks

Genie

Semantic layer

dbt

PySpark

SQL

Python

Airflow

Databricks Workflows

RBAC

RLS

Descripción del empleo

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 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

Requirements

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

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