# AI Data Enablement Engineer

**Company:** [Xenon7](https://jobs.workable.com/companies/khsAik1aNHDNR3tsuc1Jr7.md)
**Location:** Remote
**Workplace:** remote
**Department:** Delivery and Solutions

[Apply for this job](https://jobs.workable.com/view/6b3f416d-6f2a-4b3c-8d29-fea4fe8934c7)

## 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 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