Junior Data Engineer / AI Engineer

Overview

• Snowflake Data Engineering (Core Focus)
• Develop and maintain basic data models (e.g., star schema, snowflake schema).
• Build and support ELT pipelines using Snowflake and SQL.
• Write efficient SQL queries for data transformations.
• Work with structured and semi-structured data (JSON, CSV, Parquet, etc.).
• Assist with warehouse optimization (performance, cost, clustering).
• Support implementation of RBAC and basic data governance standards.
• AI & Cortex LLM Support (Guided Role)
• Assist in developing AI agents leveraging Snowflake Cortex LLM.
• Learn and support RAG (Retrieval-Augmented Generation) workflows.
• Work with vector embeddings and help build semantic search components.
• Participate in prompt engineering, testing, and output validation.
• Support evaluation of AI-generated responses for quality and security.
• Conversational & Analytics Solution Support
• Contribute to building conversational analytics and natural-language query features.
• Support integration of Snowflake data models with BI tools (Power BI / Tableau).
• Assist in transforming business KPIs into dashboards and datasets.
• Ensure data accuracy, consistency, and performance.
• Collaboration & Delivery
• Work with senior engineers, leads, and architects on design and implementation.
• Participate in Agile ceremonies (scrum meetings, sprint planning).
• Document data models, transformations, scripts, and solution components.
• Assist with production support and troubleshooting.
  
  

• 0–4 years of experience in Data Engineering / Analytics Engineering.
• Hands-on exposure to SQL (joins, aggregations, window functions preferred).
• Basic knowledge of Snowflake (or any cloud data warehouse).
• Working knowledge of Python for data processing.
• Understanding of data modeling concepts.
• Exposure to AI/LLM platforms (Snowflake Cortex or similar is a plus).
• Familiarity with BI tools (Power BI / Tableau).
  
  

• Exposure to semantic search or vector databases.
• Understanding of RAG pipelines or LLM concepts.
• Experience with semi-structured data formats (JSON, Parquet).
• Basic understanding of data governance/security practices.
• Domain exposure in HR, Sales, Finance, or Supply Chain analytics.
  
  

• Exposure to semantic search or vector databases.
• Understanding of RAG pipelines or LLM concepts.
• Experience with semi-structured data formats (JSON, Parquet).
• Basic understanding of data governance/security practices.
• Domain exposure in HR, Sales, Finance, or Supply Chain analytics.