Job Description:

We are seeking an experienced Senior Data Engineer with 6-11 years of experience in designing and building enterprise-scale data platforms using Snowflake, Python, and modern ELT technologies. The ideal candidate should have strong expertise in developing scalable data pipelines and data warehouses while leveraging Generative AI technologies such as Large Language Models (LLMs), LangChain, LangGraph, and Agentic AI frameworks to build intelligent, AI-powered data solutions.

The role requires expertise in Snowflake architecture, ELT development, cloud-based data engineering, DevOps practices, and the ability to integrate GenAI capabilities into modern data platforms.

Key Responsibilities:

- Design, develop, and maintain enterprise-scale data pipelines using Snowflake as the primary cloud data platform.

- Build highly scalable and optimized ELT/ETL workflows for structured and semi-structured data.

- Develop efficient data ingestion, transformation, and loading processes using Snowflake SQL and Python.

- Design dimensional data models, star schemas, and enterprise data warehouse architectures.

- Optimize Snowflake performance through clustering, partitioning, query tuning, materialized views, and warehouse optimization.

- Implement robust CI/CD pipelines and DevOps best practices for automated deployments.

- Develop reusable data engineering frameworks and components for enterprise analytics.

- Integrate Generative AI capabilities into data engineering workflows to automate data processing and business intelligence.

- Build intelligent applications using Large Language Models (LLMs) for enterprise data use cases.

- Develop AI-powered workflows using LangChain and LangGraph frameworks.

- Design and implement Agentic AI solutions capable of autonomous reasoning and multi-step task execution.

- Build Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge management.

- Integrate vector databases and semantic search capabilities with enterprise data platforms.

- Collaborate with Data Scientists, AI Engineers, Business Analysts, and Product teams to deliver AI-driven solutions.

- Ensure high availability, security, governance, and reliability of enterprise data platforms.

- Monitor pipeline performance and implement continuous optimization strategies.

- Participate in architecture discussions and recommend scalable cloud-native solutions.

- Ensure compliance with enterprise security, governance, and data quality standards.

Required Technical Skills:

1. AI & Generative AI Expertise:

- Large Language Models (LLMs)

- LangChain

- LangGraph

- Agentic AI Frameworks

- Prompt Engineering

- AI Orchestration

- Retrieval-Augmented Generation (RAG)

- Vector Databases (Pinecone, FAISS, ChromaDB, Weaviate, etc.)

- Semantic Search

- Embedding Models

- AI Workflow Automation

- OpenAI APIs / Azure OpenAI / Anthropic Claude / Gemini (preferred)

- Multi-Agent Systems

- AI Agents for Data Engineering

- Model Evaluation & Optimization

2. Snowflake Expertise:

- Snowflake Architecture

- Virtual Warehouses

- Secure Data Sharing

- Time Travel

- Zero Copy Cloning

- Streams & Tasks

- Snowpipe

- Query Optimization

- Data Security

- Role-Based Access Control (RBAC)

- Performance Tuning

- Cost Optimization

- Data Governance

Qualifications & Experience:

- Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, or a related field.

- Master's degree is preferred.

- 6-11 years of experience in Data Engineering.

- 5+ years of experience designing enterprise data warehouses on Snowflake.

- Strong hands-on experience in Python development.

- Experience building enterprise ELT/ETL pipelines.

- Hands-on experience with DevOps and CI/CD practices.

- Practical experience with Generative AI and Agentic AI frameworks.

- Experience integrating LLM-powered solutions into enterprise data platforms.

- Experience working in Agile/Scrum development environments.

- Strong expertise in modern cloud-native data engineering.

- Passion for Generative AI and intelligent data solutions.

- Excellent analytical and problem-solving skills.

- Ability to architect scalable enterprise data platforms.

- Strong understanding of AI-assisted automation and autonomous agent workflows.

- Excellent communication and stakeholder management skills.

- Ability to mentor junior engineers and lead technical discussions.

- Experience working in cross-functional and globally distributed teams.