AI Agent Engineer – Data Engineering

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 AI Agent Engineer

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 lifecycles—covering ingestion through deployment—via 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.