AI Data Solutions Architect

The AI Data Solutions Architect is a senior technical role within the AI Center of Excellence, responsible for designing and governing the data architecture that underpins all AI and machine learning initiatives This individual will own the end-to-end data strategy for AI projects - determining where data should reside, how it flows between systems, and how it is structured to maximize value for AI workloads. They will maintain a comprehensive data lineage and usage inventory, ensuring the organization has clear visibility into where data is sourced, transformed, stored, and consumed.

15+ years in Data Architecture/Data Engineering, strong Snowflake or BigQuery background, experience building enterprise data lakes/lakehouses, proven AI/GenAI architecture experience (RAG, vector databases, embeddings, unstructured data), strong governance/lineage expertise, and executive-level communication skills., * 8+ years of experience in data architecture, data engineering, or enterprise data management.

• 3+ years of hands-on experience designing data solutions for AI/ML workloads.
• Deep expertise with cloud data platforms, particularly Snowflake, Google BigQuery, or equivalent.
• Experience with enterprise data lake or data mesh architectures.

• Strong understanding of data modeling (dimensional, graph, document), ETL/ELT pipelines, and data integration patterns.

• Experience with data governance frameworks, data cataloging tools, and lineage tracking.
• Experience with AI/ML data requirements including feature engineering, vector embeddings, retrieval augmented generation (RAG), and unstructured data processing.
• Proficiency in SQL; working knowledge of Python or similar scripting languages.

• Excellent communication skills with the ability to translate complex data concepts for non-technical stakeholders.

• Bachelor’s degree in Computer Science, Data Science, Information Systems, or related field. Preferred:

• Experience with Snowflake’s advanced features (Snowpark, Cortex, data sharing, dynamic tables).
• Hands-on experience with Google Cloud Platform services (Vertex AI, Cloud Storage, Pub/Sub, Dataflow).
• Familiarity with knowledge graph technologies, semantic layers, or ontology design for AI applications.
• Background in retail, automotive, or RV/dealership industry data environments.
• Relevant certifications (e.g., Snowflake SnowPro, Google Cloud Platform Professional Data Engineer, AWS Data Analytics).
• Experience with MCP (Model Context Protocol) or similar AI-to-data integration frameworks