Principal Data & AI Architect

The Hartford•Hartford, CT

17h•Hybrid

About The Position

The Principal Data & AI Architect is a strategic technical leader responsible for defining and advancing enterprise data architecture so data is trusted, connected, reusable, and ready for analytics, machine learning, Generative AI, and agentic applications. The role develops line-of-business and cross-domain architectures spanning data products, semantic layers, cloud data platforms, open lakehouse technologies, metadata, integration, and AI-ready data access. The environment is multi-cloud, with Snowflake on AWS as the primary enterprise data platform and Google Cloud providing strategic AI, machine learning, agent, and advanced analytics capabilities. As a senior individual contributor, this architect influences enterprise technology decisions, establishes reusable architecture patterns, guides complex initiatives, and mentors architects and engineers. The role also involves hands-on experimentation and prototyping to demonstrate potential data and AI capabilities. The position has a hybrid arrangement requiring three days per week in the office in Charlotte, NC; Hartford, CT; Columbus, OH; or Chicago, IL. Candidates must be eligible to work in the United States without company sponsorship.

Requirements

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.
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 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, including the ability to lead complex technical initiatives through influence.
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.
Experience with ontology or graph platforms and with Model Context Protocol, semantic APIs, LLMOps, MLOps, DataOps, AI evaluation, or Responsible AI practices.
Must be eligible to work in the United States without company sponsorship.

Nice To Haves

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.

Responsibilities

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.
Design governed data foundations that make structured, semi-structured, and unstructured data discoverable, understandable, accessible, and suitable for analytics, machine learning, Generative AI, retrieval-augmented generation, and agentic solutions.
Define and evolve semantic models, governed metrics, business vocabularies, ontologies, and knowledge graphs; establish guidance for semantic governance, lifecycle management, interoperability, lineage, provenance, and access control.
Architect data and semantic capabilities for conversational and agentic analytics, including natural-language data access, proactive insight generation, anomaly and root-cause analysis, and decision support; define controls for grounding, query validation, traceability, observability, evaluation, and human review.
Establish secure and cost-effective patterns for integrating Snowflake on AWS with Google Cloud AI and analytics services using approved access methods.
Define standards and adoption guidance for Apache Iceberg, object storage, open catalogs, table lifecycle management, and multi-engine interoperability.
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.
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.
Lead architecture reviews, assess emerging technologies, conduct hands-on experiments and rapid prototypes, and document architecture decisions.
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.
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.