AI Data Enablement Engineer

• 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

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