AI Data Enablement Engineer
Company: Xenon7
Role summary
AI Data Enablement Engineer is a role at Xenon7 in Hyderabad, Telangana, India (remote). Posted 2026-08-23. Experience level: 5-10.
- Job title
- AI Data Enablement Engineer
- Company
- Xenon7
- Location
- Hyderabad, Telangana, India
- Work arrangement
- remote
- Category
- Software Engineering
- Experience level
- 5-10
- Date posted
- 2026-08-23
You’ll get to shape how data powers the next generation of intelligent systems while working entirely from home. It’s a fantastic opportunity for someone who loves the intersection of data engineering and machine learning to build scalable pipelines at a high level.
Job 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.
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
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
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