Resources by role — AI Data Solutions Architect
AI Data Solutions Architect
A senior technical role responsible for designing and governing the data architecture that underpins all AI and machine learning initiatives — determining where data will reside, how it flows between systems, and how it is structured to maximize value for AI workloads.
What this role does
Key responsibilities
AI Data Architecture & Design
- Define and maintain the enterprise data architecture for all AI and machine learning projects, including data lake, warehouse, and feature store strategies
- Design data models, schemas, and pipelines optimized for AI/ML consumption across Snowflake, BigQuery, Postgres, and supporting platforms
- Establish standards for data ingestion, transformation, storage, and retrieval that support real-time and batch AI workloads
- Architect integration patterns that connect structured and unstructured data sources (Snowflake, Salesforce, SharePoint, PDFs, APIs) into unified, AI-ready datasets
Data Lineage, Governance & Inventory
- Build and maintain a comprehensive data catalog and lineage map that documents where every critical data asset lives, how it is used, and which AI models depend on it
- Partner with Data Engineering and Analytics teams to ensure data quality, consistency, and compliance across all AI data pipelines
- Implement metadata management practices and tooling to enable self-service discovery and impact analysis
- Define data classification, access controls, and retention policies for AI-specific datasets in alignment with security and regulatory requirements
AI Project Delivery & Collaboration
- Serve as the embedded data architect across all AI projects, ensuring each initiative has a sound data foundation from design through deployment
- Collaborate with AI/ML engineers, agent developers, and platform teams to translate business requirements into data architecture decisions
- Evaluate and recommend data technologies, connectors, and integration tools that accelerate AI delivery (e.g., vector databases, RAG pipelines, embedding stores)
- Support the buildout of the enterprise data lake initiative, consolidating disparate data sources for AI and analytics workloads
Strategic Planning & Standards
- Develop and maintain data architecture standards, reference architectures, and design patterns tailored for AI use cases
- Create and present architecture proposals, data flow diagrams, and technical documentation to both technical and executive audiences
- Stay current on emerging data technologies, cloud-native data services, and AI infrastructure patterns; advise leadership on adoption opportunities
- Contribute to development of AI technology standards and roadmap for data infrastructure
What this role requires
Required qualifications
- 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
- 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
- Familiarity 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
Nice to have
Preferred qualifications
- Experience with Snowflake's advanced features (Snowpark, Cortex, data sharing, dynamic tables)
- Hands-on experience with GCP services (Vertex AI, Cloud Storage, Pub/Sub, Dataflow)
- Familiarity with knowledge graph technologies, semantic layers, or ontology design for AI applications
- Experience with enterprise data lake or data mesh architectures
- Background in retail, automotive, or RV/dealership industry environments
- Relevant certifications (e.g., Snowflake, SnowPro, GCP Professional Data Engineer, AWS Data Analytics)
- Experience with MCP (Model Context Protocol) or similar AI-to-data integration frameworks