AI Data Enablement Engineer

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 aData Enablement Engineerto design, build, and operate the trusted datasets, semantic models, and embedded AI experiences that make this possible.

This is adata 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 buildAI-ready data productson Databricks — trusted datasets with well-defined business semantics, KPIs, hierarchies, and business glossary alignment
• Implementsemantic layersand governed datasets that support both traditional BI consumption and natural-language querying by business users
• Deploy and operateDatabricks Genie spaceswith Unity Catalog, tuning them for accuracy, adoption, and business relevance
• BuildRAG pipelines and conversational analytics applicationsgrounded 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
• Implementdata governance— RBAC, row/column-level security, masking, lineage, auditability, catalog and metadata management — in a regulated pharma environment
• Optimizecost and performanceon 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 engineeringon 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 (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
• Orchestrationwith Airflow, Databricks Workflows, or equivalent
• Data governance in regulated environments— RBAC, RLS, masking, lineage, auditability
• Experience integratingstructured 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 Engineerswho list Cortex or Genie as a skill but haven't shipped it in production
• AI/GenAI engineerswhose 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