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 DoDesign and buildAI-ready data productson Snowflake and/or Databricks - trusted datasets with well-defined business semantics, KPIs, hierarchies, and business glossary alignmentImplementsemantic layersand governed datasets that support both traditional BI consumption and natural-language querying by business usersDeploy and operateSnowflake Cortexcapabilities (Cortex Analyst, Cortex Search, Cortex Agents, Cortex LLM Functions) and/orDatabricks Genie spaceswith Unity Catalog, tuning them for accuracy, adoption, and business relevanceBuildRAG pipelines and conversational analytics applicationsgrounded 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 onImplementdata governance- RBAC, row/column-level security, masking, lineage, auditability, catalog and metadata management - in a regulated pharma environmentOptimizecost 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 trustMust-Have Experience5+ years hands-on data engineeringon 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 stackOrchestrationwith Airflow, Databricks Workflows, or equivalentData governance in regulated environments- RBAC, RLS, masking, lineage, auditabilityExperience integratingstructured 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 Engineerswho list Cortex or Genie as a skill but haven’t shipped it in productionAI/GenAI engineerswhose 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-*****-Ljbffr

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