Principal Data & AI Architect

Primary Job Responsibilities

Strategic Data Architecture Leadership: Define line-of-business and cross-domain data architectures aligned with enterprise data, AI, cloud, and technology strategies. Translate business needs into target-state architectures, roadmaps, standards, and reusable patterns.

AI-Ready Data Architecture: Design governed data foundations that make structured, semi-structured, and unstructured data discoverable, understandable, accessible, and fit for analytics, machine learning, Generative AI, retrieval-augmented generation, and agentic solutions.

Semantic Layer Architecture: Define and evolve semantic models, governed metrics, business vocabularies, ontologies, and knowledge graphs that provide consistent business meaning across analytics and AI. Establish guidance for semantic governance, lifecycle management, interoperability, lineage, provenance, and access control.

Agentic Analytics: Architect the data and semantic capabilities required for conversational and agentic analytics, including natural-language data access, proactive insight generation, anomaly analysis, root-cause analysis, and decision support. Define controls for grounding, query validation, traceability, observability, evaluation, and human review.

Multi-Cloud Data Architecture: Establish secure and cost-effective patterns for integrating Snowflake on AWS with Google Cloud AI and analytics services using selective replication, federation, APIs, streaming, caching, open table formats, or other approved access methods.

Apache Iceberg and Open Lakehouse: Define standards and adoption guidance for Apache Iceberg, object storage, open catalogs, table lifecycle management, and multi-engine interoperability across Snowflake, AWS, Google Cloud, Spark, and related technologies.

Data Product and Integration Architecture: Define architecture for governed data products, including domain boundaries, data contracts, canonical entities, quality expectations, metadata, lineage, access interfaces, service levels, and lifecycle ownership. Guide batch, streaming, event-driven, API, change-data-capture, and federated integration patterns.

Governance, Security, Privacy, and Responsible AI: Embed data quality, metadata, lineage, security, privacy, retention, regulatory compliance, provenance, and responsible AI requirements into architecture designs. Ensure AI-enabled solutions support traceability, explainability, reliability, auditability, and appropriate human oversight.

Technology Leadership and Influence: Lead architecture reviews, assess emerging technologies, and perform focused hands-on experiments and rapid prototyping to validate concepts, evaluate feasibility, and demonstrate the art of the possible. Document architecture decisions and create standards, reference architectures, decision trees, and implementation patterns. Influence senior stakeholders and drive adoption across architecture, engineering, analytics, data science, product, governance, privacy, and cybersecurity teams.

Mentorship and Community Leadership: Mentor architects and engineers, elevate technical practices, and contribute to architecture forums and communities of practice. Communicate complex architecture choices in terms of business value, risk, interoperability, reliability, and cost.

Skills

• Expert knowledge of enterprise data architecture, conceptual and logical data modeling, data products, metadata, data integration, data quality, and data governance.
• Deep experience designing enterprise-scale data solutions using Snowflake, AWS, and Google Cloud.
• Strong experience with Google Cloud data and AI services, such as BigQuery, Gemini Enterprise Agent Platform, Knowledge Catalog, Dataflow, and managed Spark.
• Demonstrated experience designing multi-cloud or cross-cloud data architectures and evaluating data locality, latency, replication, security, performance, resiliency, operational complexity, and cost tradeoffs.
• Experience with Apache Iceberg, open table formats, cloud object storage, catalog architectures, table maintenance, and multi-engine data access.
• Experience designing semantic layers, semantic models, enterprise metrics, business vocabularies, or natural-language data access capabilities.
• Knowledge of ontology and knowledge graph concepts, including taxonomies, entities, relationships, graph traversal, inference, entity resolution, RDF/OWL, SPARQL, SHACL, or property-graph approaches.
• Experience enabling AI/ML, Generative AI, retrieval-augmented generation, vector search, GraphRAG, conversational analytics, or agentic applications.
• Strong understanding of batch, streaming, event-driven, API, change-data-capture, and data-federation patterns.
• Strong knowledge of cloud security, privacy, lineage, observability, resilience, performance optimization, and cost management.
• Demonstrated ability to work hands-on with emerging data and AI technologies, rapidly build proofs of concept and prototypes, learn through experimentation, and translate findings into clear demonstrations, architecture guidance, and scalable implementation patterns.
• Proficiency with SQL and experience with Python, PySpark, Snowpark, or comparable data-engineering technologies.
• Excellent communication, presentation, facilitation, and influencing skills, with demonstrated ability to lead complex technical initiatives through influence.
• Demonstrated ability to challenge assumptions, identify risks and unintended consequences, evaluate alternative viewpoints, and provide balanced recommendations based on Data and AI strategic objectives, technical considerations, and business outcomes.

Required Experience

• Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, Artificial Intelligence, a related field, or equivalent work experience.
• 10+ years of experience in data architecture, enterprise architecture, data engineering, analytics architecture, or a related discipline.
• Significant experience designing enterprise data platforms and data solutions in complex or regulated environments.
• Demonstrated experience leading architecture for large, cross-functional initiatives spanning data platforms, cloud services, analytics, and AI.
• Experience with ontology or graph platforms and with Model Context Protocol, semantic APIs, LLMOps, MLOps, DataOps, AI evaluation, or Responsible AI practices.

Preferred Qualifications

• Experience in property and casualty insurance, employee benefits, financial services, or another regulated industry.
• Professional certifications in Snowflake, AWS, Google Cloud, data architecture, or data engineering.
• Experience with Snowflake Semantic Views, Cortex Analyst, Cortex Search, Cortex Agents, or Snowflake Open Catalog.
• Experience with Google Cloud lakehouse capabilities, BigQuery, Gemini, Gemini Enterprise Agent Platform, or graph technologies.