Related skills
snowflake python dbt airflow vector databases📋 Description
- Own end-to-end data architecture for the Data Warehouse Foundation.
- Lead cross-department data modeling for AI and analytics.
- Design contextual intelligence: RAG, vectors, knowledge ingestion.
- Build agentic data integration: real-time access, memory, orchestration.
- Own AI/ML feature layer: feature store and pipelines.
- Govern GPT context layer, data freshness, SLAs, reuse standards.
🎯 Requirements
- Bachelor's in CS, Data Eng, IS, or related field.
- 7–10 years in data engineering/architecture; 3+ years AI infra.
- Snowflake expertise; dbt, Airflow, ELT/ETL.
- AI-ready data architectures: vector DBs, embeddings, RAG.
- Strong data modeling: dimensional, normalized, AI schemas.
- Python and SQL proficiency; AWS cloud infra.
🎁 Benefits
- Experience in private equity-backed SaaS organizations.
- Experience with agentic AI frameworks (LangGraph, Mastra, etc.).
- Production-scale RAG architectures; vector store selection.
- Agent memory architectures and state persistence.
- AI governance and data compliance familiarity.
- Tines or no-code/low-code orchestration experience.
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