
Snowflake Developer
2 hours ago
Role: Senior Snowflake 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 Snowflakes 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, and row/column-level security.
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