AI Data Enablement Engineer
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 71d ago
The date the source published, not the day we noticed it (2026-08-23). Last seen at its source 1h ago.
We have tracked this listing since 4 Sep 2026 (58 days). The employer's own board has carried it every time we have read it, most recently 1 hour ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
India, Serbia, Croatia, Spain, Italy
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay, by region:
- Europe €60.1k–107.7k/yr (370 listings, all levels)
- Asia $65.8k–165k/yr (50 listings, all levels)
Middle 50% of Engineering listings that do state pay in each region, in the currency most of them state. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
Workable employer's own board first seen 58d ago · last seen 1h ago
The listing
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 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 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, 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 — Databricks demonstrated in real project delivery, not skill-list-only
- 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 (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, 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
- 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