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 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 Requirements 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 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