AI Data Enablement Engineer

Xenon7; Hyderabad; On-site; INR 2,400,000 - 3,600,000; Full time

Job summary

Xenon7 is hiring a Data Enablement Engineer to design and operate trusted datasets, semantic models, and embedded AI experiences atop an enterprise data platform. You will focus on data foundation, governance, and robust ETL/ELT pipelines to enable natural-language analytics for business users.

This is a data platform engineering role, not a model-building one. You will work with Snowflake/Databricks, implement semantic layers, RBAC, and cost/performance optimizations in a regulated pharma

Qualifications

• 5+ years hands-on data engineering on cloud data platforms — Snowflake and/or Databricks.
• Direct hands-on experience with either Snowflake Cortex or Databricks Genie.
• Semantic layer / trusted data product delivery with KPI definitions.
• dbt, PySpark, Snowpark, SQL, Python across the modern data stack.
• Orchestration with Airflow, Databricks Workflows, or equivalent.
• Data governance in regulated environments — RBAC, RLS, masking, lineage.

Responsibilities

• Design and build AI-ready data products on Snowflake and/or Databricks with defined semantics, KPIs, hierarchies, and business glossary alignment.
• Implement semantic layers and governed datasets for BI and natural-language querying.
• Deploy and operate Cortex/Genie capabilities with Unity Catalog and tune for accuracy and adoption.
• Build RAG pipelines and conversational analytics apps grounded in governed data.
• Engineer robust ETL/ELT pipelines (dbt, Airflow, Snowpark, PySpark).
• Implement data governance in regulated pharma environments (RBAC, masking, lineage).
• Partner with Finance to translate requirements into semantic models and trusted data products.

Skills

Data engineering

Python

SQL

dbt

PySpark

Airflow

ETL/ELT

Tools

Snowflake Cortex

Databricks Genie

Snowflake

Databricks

Unity Catalog

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