Own the design and delivery of data + GenAI solutions where Snowflake is the primary platform.

Expect a practical split of ~60% data engineering/Snowflake optimization and ~40% GenAI/Cortex work (good to have).

What youll do :

- Build and productionize data pipelines in Snowflake (Snowflake SQL, Tasks/Streams, Snowpark/Python) with strong SLAs, cost controls, and monitoring.

- Use Snowflake Cortex (LLM functions, vector search, model inference) to ship RAG/analytics assistants, summarization, and workflow automation.

- Prototype quickly, then harden to production with CI/CD, logging, alerting, and access controls.

- Integrate external LLM services or vector stores when Snowflake-native features arent the best fit, keeping data gravity in Snowflake.

- Apply governance and security best practices (RBAC, masking, tags, row-level/column-level security).

- Collaborate with data product owners and analysts to scope use cases, estimate effort, and measure impact.

- Document patterns and coach teammates on Snowflake/Cortex usage.

Must-have qualifications :

- 6- 8 years in data engineering/analytics (or equivalent impact), including 1+ year building in Snowflake in production.

- Strong SQL and Python; hands-on with Snowpark and UDFs (Python preferred).

- Practical experience with at least one Snowflake AI capability : Cortex LLM functions, vector search, or model inference (POCs are fine).

- Proven ability to ship : from prototype production with quality, cost, and performance in mind.

- Working knowledge of data security/governance and performance tuning in Snowflake.

Nice to have :

- Snowflake certifications (SnowPro Core/Advanced/Data Engineering).

- Experience with RAG architectures, embeddings, LangChain/LlamaIndex, or ML lifecycle tools (MLflow).

- dbt, Git-based CI/CD, Docker; basic observability for data/LLM systems.

- Data viz (e.g., Power BI/Tableau) or building simple REST/Streamlit apps for demos.

- Prior work with Azure OpenAI, GCP Vertex AI, Databricks, or other cloud AI stack.