Agentic AI Data Engineer - Cortex AI

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Job IDs                         : P-101830 (OR) P-101823 (OR) P-101820

Job Description for Agentic AI Data Engineer - Cortex AI across PAN India:

Primary Focus: 

• Development, orchestration, and maintenance of the agentic framework that automates the engineering lifecycle. 

Secondary Focus: 

• Technical execution and delivery of the Data Engineering lifecycle (Discover, Build, Test, Deploy). 
• 6+ Years Experience Snowflake Data Engineer with CoCo exp 

Key Responsibilities: 

• Develop and refine agentic workflows using the Snowflake CoCo (CoCo) harness. Integrate LLMs, Cortex AI models, and orchestration logic to automate discovery, impact analysis, build, and testing stages. 
• Build "Specification Driven Development" (SDD) capabilities, creating machine-readable instructions for data products. 
• Collaborate with Data Engineers to convert manual engineering tasks into autonomous agentic patterns. 
• Drive technical execution for data product delivery and datasets optimized for enterprise consumption. 
• Oversee core engineering layer components, including SQL Stored Procedures, DBT models, and DataOps.Live. 
• Maintain code quality and minimize defects while ensuring full alignment with EDO governance standards Contribute to automated engineering lifecyclescovering ingestion through deploymentvia platform-agnostic frameworks. 

Essential Skills: 

• AI/GenAI Engineering: Deep experience with Cortex Skills, LLM integration, and Retrieval-Augmented Generation (RAG) concepts. 
• Agentic Frameworks: Proficiency in building and maintaining agentic workflows (e.g., CoCo CLI, Model Context Protocol). 
• Foundational Knowledge: Solid background in data engineering (SQL, Python) to bridge the gap between AI capabilities and data platform requirements. 
• Core Engineering Competencies: Advanced technical mastery of SQL, DBT models, and DataOps.Live (DOL) orchestration. 
• Platform Expertise: Comprehensive proficiency across the Snowflake ecosystem and its integrated service offerings. 
• Data Product Lifecycle: Strategic command of multi-tier architectures (Bronze, Silver, Gold) and the management of enterprise-grade data products.