Snowflake Data Engineer

A Senior Snowflake Developer with Cortex experience is responsible for designing,

building, and optimizing data solutions on the Snowflake platform, with a specific

focus on leveraging Snowflake Cortex for AI-driven features like RAG (Retrieval-

Augmented Generation), LLM functions, and vector search. Key responsibilities

include developing scalable ETL/ELT pipelines, creating data models, implementing

AI assistants, and coaching other team members, all while adhering to best practices

in performance, cost control, and security.

Skills:

• Bachelors degree in Computer Science, Software Engineering, Information
• Technology, or related field required.
• At least 4 years of experience in data development and solutions in highly complex data environments with large data volumes.
• At least 3 years of SQL / PLSQL experience with the ability to write ad-hoc
• and complex queries to perform data analysis.
• At least 3-5 years of experience developing data pipelines and data
• warehousing solutions using Python and libraries such as Pandas, NumPy 
• etc.
• Familiarity with Snowflake’s architecture, including virtual warehouses, data
• storage layers, and compute scaling.

Key Responsibilities:

• Design, build, and deploy data solutions using Snowflake, Snowflake Cortex
• (including Cortex Analyst, LLM functions, and vector search), and Snowpark
• (Python). 
• Develop AI solutions to automate tasks, provide insights, and build AI-
• powered assistants or chatbots. 
• Create and maintain high-quality, scalable ELT/ETL pipelines with strong
• SLAs, cost controls, and monitoring. 
• Design and implement Snowflake data models and schemas to support
• complex analytical workloads. 
• Write clean, efficient Python and SQL code, conduct code reviews, and
• mentor junior developers. 
• Optimize existing data pipelines and queries for performance and cost-
• efficiency. 
• Implement and maintain CI/CD pipelines for automated testing and
• deployment of data solutions. 
• Work with cross-functional teams, including data analysts and product
• owners, to gather requirements and deliver data-driven value. 
• Apply and enforce data governance and security best practices, including
• role-based access control (RBAC), masking,