AI Data Enablement Engineer

Hyderabad

5+ yrs exp

Flexible

Posted 6d ago

2 views

Actively Hiring

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Job Description

AI Data Enablement Engineer

Xenon7 · Hyderabad, India (Hybrid) · — · Posted 2026-08-23

Workplace: hybrid
Department: Novartis

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

Requirements

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

Apply

Apply at Xenon7

Key Requirements & Skills

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

Apply

Apply at Xenon7

Frequently Asked Questions

How to apply for AI Data Enablement Engineer at Xenon7?

Click the "Apply on Company Website" button on this page to submit your application directly on the employer's official portal.

What is the salary for this role?

Salary details will be discussed during the interview.

What experience is required?

5+ yrs of experience is required.

Is this position still open?

Yes, currently active and accepting applications.

AI Data Enablement Engineer

Check Your Resume Match Score

Apply for this position

Job Description

AI Data Enablement Engineer

Description

Data Enablement Engineer

data platform engineering role

What You'll Do

AI-ready data products

semantic layers

Snowflake Cortex

Databricks Genie spaces

RAG pipelines and conversational analytics applications

data governance

cost and performance

Requirements

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

structured and unstructured data

Nice to Have

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

Apply

Key Requirements & Skills

AI-ready data products

semantic layers

Snowflake Cortex

Databricks Genie spaces

RAG pipelines and conversational analytics applications

data governance

cost and performance

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

structured and unstructured data

Nice to Have

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

Apply

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