AI Engineer with snowflake
Job Description
AI Engineer focused on building and operationalizing agents on Snowflake (Snowflake Cortex / Snowflake Intelligence). The role involves designing agent architectures, connecting them to enterprise data in Snowflake, and orchestrating tools, prompts, and workflows to solve real business problems. You will collaborate with data engineers, analysts, and business stakeholders to translate use cases into robust, secure agents that run reliably in production.
Skills
AI, agents, and LLMs
- Handson with Snowflake AI features: Snowflake Cortex / Intelligence, agents, and functions (or clear adjacent LLM/agentic experience with willingness to specialize in Snowflake).
- Experience with LLMs and agents: prompt design, tool calling/orchestration, AI Functions, RAG, evaluation, and guardrails.
Analytics & Semantic layer
- Actively use dbt in Snowflake: building modular models, documenting data, implementing tests, and linking analytics to downstream agents and apps.
- Worked with Snowflake semantic views (or similar semanticlayer concepts) to expose consistent, businessready data that agents can query without everyone reinventing the same logic.
Snowflake platform expertise
- Strong Snowflake experience: roles/warehouses, performance tuning, secure data sharing, tasks, time travel, semistructured data.
- Advanced SQL: complex joins, window functions, CTEs, incremental patterns (MERGE, upserts), and data quality checks.
- Solid data engineering foundations: data modeling, ELT in Snowflake, orchestration concepts (e.g., dbtstyle workflows or similar), and CI/CD for data/analytics code.
- Experience with Terraform (or equivalent IaC) to manage Snowflake objects, environments, and access patterns in a repeatable, versioned way.
Programming and Cloud
- Programming: Python for building and integrating agents, APIs, and automation around Snowflake.
- Cloud: at least one major cloud (AWS/Azure/GCP), working with APIs, identity/permission models, and secret management.
Soft skills
- Product mindset: ability to work with stakeholders, refine use cases, design conversational flows, and iterate based on feedback.
- Good communication: clear documentation, ability to explain tradeoffs and constraints to nontechnical stakeholders.
