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

• 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

• 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

• 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

Este anuncio proviene de Indeed. Ver anuncio original ↗