Key Responsibilities

1. Snowflake Data Architecture & Modeling

• 
  Design scalable,
  performance-optimized data models on Snowflake (Star schema, Snowflake
  schema, Data Vault).
• 
  Build and optimize ELT
  pipelines using Snowflake-native capabilities.
• 
  Implement data governance,
  security policies, and role-based access control (RBAC).
• 
  Perform performance
  tuning, clustering optimization, and warehouse sizing strategies.
• 
  Ensure cost-efficient
  Snowflake implementation.

2. AI & Snowflake Cortex LLM Implementation

• 
  Develop and deploy AI
  agents using Snowflake Cortex LLM.
• 
  Design and implement
  Retrieval-Augmented Generation (RAG) frameworks grounded on enterprise
  data.
• 
  Build vector embeddings,
  semantic search layers, and contextual response frameworks.
• 
  Apply prompt engineering
  and guardrails for enterprise AI use cases.
• 
  Ensure AI outputs are
  explainable, secure, and business-aligned.

3. Conversational Insights Development

• 
  Build conversational
  analytics solutions enabling natural language interaction with enterprise
  datasets.
• 
  Deliver persona-based
  insights tailored to functional stakeholders.
• 
  Integrate structured and
  semi-structured data into unified analytics models.
• 
  Collaborate with UI/BI
  teams for dashboard and insight delivery.

4. Technical Leadership & Delivery Ownership

• 
  Lead a small team (3–6
  engineers) and provide architectural guidance.
• 
  Review code, enforce best
  practices, and ensure quality standards.
• 
  Own end-to-end delivery
  from requirements to deployment.
• 
  Participate in effort
  estimation, sprint planning, and stakeholder communication.
• 
  Mentor junior engineers on
  Snowflake and AI implementation.

5. Business & Functional Collaboration

• 
  Translate functional
  requirements from HR, Sales, Finance, and Supply Chain into scalable data
  solutions.
• 
  Build KPI frameworks and
  analytical models aligned to business goals.
• 
  Identify automation
  opportunities using AI-driven insights.

Required Qualifications

• 
  7–10 years of experience
  in Data Engineering / Analytics Engineering / AI Engineering.
• 
  4+ years hands-on
  experience with Snowflake Data Warehouse.
• 
  Strong expertise in data
  modeling (Dimensional modeling preferred).
• 
  Hands-on experience with
  Snowflake Cortex LLM or similar LLM platforms.
• 
  Experience building
  RAG-based or conversational AI solutions.
• 
  Strong SQL and Python
  skills.
• 
  Understanding of data
  governance, security, and performance optimization.
• 
  Experience working in
  Agile delivery environments.

Preferred Qualifications

• 
  Snowflake certification.
• 
  Experience with BI tools
  (Power BI / Tableau).
• 
  Exposure to MLOps and
  model deployment practices.
• 
  Domain knowledge in at
  least two functional areas (HR, Sales, Finance, Supply Chain).

Requirements

Required Qualifications

• 
  7–10 years of experience
  in Data Engineering / Analytics Engineering / AI Engineering.
• 
  4+ years hands-on
  experience with Snowflake Data Warehouse.
• 
  Strong expertise in data
  modeling (Dimensional modeling preferred).
• 
  Hands-on experience with
  Snowflake Cortex LLM or similar LLM platforms.
• 
  Experience building
  RAG-based or conversational AI solutions.
• 
  Strong SQL and Python
  skills.
• 
  Understanding of data
  governance, security, and performance optimization.
• 
  Experience working in
  Agile delivery environments.

Preferred Qualifications

• 
  Snowflake certification.
• 
  Experience with BI tools
  (Power BI / Tableau).
• 
  Exposure to MLOps and
  model deployment practices.
• 
  Domain knowledge in at
  least two functional areas (HR, Sales, Finance, Supply Chain).