Descripción de la oferta
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 DoDesign and build AI-ready data products on Snowflake and/or Databricks — trusted datasets with well-defined business semantics, KPIs, hierarchies, and business glossary alignmentImplement semantic layers and governed datasets that support both traditional BI consumption and natural-language querying by business usersDeploy 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 relevanceBuild RAG pipelines and conversational analytics applications grounded in governed enterprise data — including Streamlit or Databricks Apps that let business users query data without writing SQLEngineer robust ETL/ELT pipelines (dbt, Airflow, Snowpark, PySpark) that produce and maintain the trusted data these AI experiences depend onImplement data governance — RBAC, row/column-level security, masking, lineage, auditability, catalog and metadata management — in a regulated pharma environmentOptimize 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 trustMust-Have Experience5+ years hands-on data engineering on cloud data platforms — Snowflake and/or Databricks demonstrated in real project delivery, not skill-list-onlyDirect 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 alignmentdbt, PySpark, Snowpark, SQL, Python — strong across the modern data stackOrchestration with Airflow, Databricks Workflows, or equivalentData governance in regulated environments — RBAC, RLS, masking, lineage, auditabilityExperience integrating structured and unstructured data (PDFs, SharePoint/Teams content, enterprise knowledge sources) into AI-enablement workflowsNice to HavePharma, life sciences, or regulated financial services domain experienceVeeva CRM, IQVIA, SAP, or clinical data source integrationStreamlit or Databricks Apps for business-facing analyticsSnowPro Advanced or Databricks Data Engineer Professional certificationLangChain, LlamaIndex, or equivalent RAG frameworksCost optimization on both compute (warehouse/cluster) and LLM (tokens/caching/routing) dimensionsWhat We're NOT Looking ForData Scientists — this role is not model training, fine-tuning, LoRA/RLHF, or ML researchPure Data Engineers who list Cortex or Genie as a skill but haven't shipped it in productionAI/GenAI engineers whose center of gravity is LangChain agents or RAG-over-documents, without a strong governed data platform foundationComputer vision, NLP model builders, or multi-agent orchestration specialists — wrong shape for this role#J-18808-Ljbffr
