{"id":1002215,"url":"https://alion.io/job/xenon-seven-ai-data-enablement-engineer-2","title":"AI Data Enablement Engineer","company":{"id":450149,"name":"Xenon Seven","domain":"xenon7.com","url":"https://alion.io/company/xenon-seven","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"contractor","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Barcelona, Spain","Sant Cugat del Vallès, Spain","Terrassa, Spain"],"countries":["ES"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":46000,"max_usd":115000,"period":"year","method":"role_country_seniority_unknown","sample_n":34},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"ETL/ELT","optional":false},{"name":"LLM","optional":false},{"name":"Platform Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SharePoint","optional":false},{"name":"SQL","optional":false},{"name":"Streamlit","optional":false},{"name":"Computer Vision","optional":true},{"name":"Fine-tuning","optional":true},{"name":"LangChain","optional":true},{"name":"LlamaIndex","optional":true},{"name":"LoRA","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"NLP","optional":true},{"name":"PEFT","optional":true},{"name":"Pure Data","optional":true},{"name":"RLHF","optional":true},{"name":"Spark","optional":true},{"name":"Veeva","optional":true}],"status":"live","first_seen_at":"2026-09-17T13:08:15Z","employer_posted_date":"2026-09-17","last_verified_at":"2026-11-04T18:32:16Z","board_verified":true,"closed_at":null,"days_open":48,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":48},"description":"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.\nThis 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.\nWhat You'll Do\nDesign and build AI-ready data products on Databricks - trusted datasets with well-defined business semantics, KPIs, hierarchies, and business glossary alignment\nImplement semantic layers and governed datasets that support both traditional BI consumption and natural-language querying by business users\nDeploy and operate Databricks Genie spaces with Unity Catalog, tuning them for accuracy, adoption, and business relevance\nBuild 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\nEngineer robust ETL/ELT pipelines (dbt, Airflow, Snowpark, PySpark) that produce and maintain the trusted data these AI experiences depend on\nImplement data governance - RBAC, row/column-level security, masking, lineage, auditability, catalog and metadata management - in a regulated pharma environment\nOptimize cost and performance on both the data platform side (warehouse sizing, cluster tuning, query optimization) and the AI side (token usage, caching, model routing)\nPartner with Finance business stakeholders to translate domain requirements into semantic models and governed data products they can trust\nRequirements\nMust-Have Experience\n5+ years hands-on data engineering on cloud data platforms - Databricks demonstrated in real project delivery, not skill-list-only\n 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)\nSemantic layer / trusted data product delivery - you have built governed datasets that business users can rely on, with KPI definitions, hierarchies, and business glossary alignment\ndbt, PySpark, Snowpark, SQL, Python - strong across the modern data stack\nOrchestration with Airflow, Databricks Workflows, or equivalent\nData governance in regulated environments - RBAC, RLS, masking, lineage, auditability\nExperience integrating structured and unstructured data (PDFs, SharePoint/Teams content, enterprise knowledge sources) into AI-enablement workflows\nNice to Have\nPharma, life sciences, or regulated financial services domain experience\nVeeva CRM, IQVIA, SAP, or clinical data source integration\nStreamlit or Databricks Apps for business-facing analytics\nDatabricks Data Engineer Professional certification\nLangChain, LlamaIndex, or equivalent RAG frameworks\nCost optimization on both compute (warehouse/cluster) and LLM (tokens/caching/routing) dimensions\nWhat We're NOT Looking For\nData Scientists - this role is not model training, fine-tuning, LoRA/RLHF, or ML research\nPure Data Engineers who list Cortex or Genie as a skill but haven't shipped it in production\nAI/GenAI engineers whose center of gravity is LangChain agents or RAG-over-documents, without a strong governed data platform foundation\n Computer vision, NLP model builders, or multi-agent orchestration specialists - 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