Principal Data & AI Architect

Principal Data & AI Architect### Principal Data & AI Architect Apply:
Hybrid:
Hartford, CT:
Charlotte, NC:
Columbus OH-Worth Ave:
Chicago, IL:
Full time:
Posted Today:
R2627424

Dir Data Architecture - GT06AE
We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.
As a Principal Data & AI Architect, you will be a strategic technical leader responsible for defining and advancing data architecture that makes enterprise data trusted, connected, reusable, and ready for analytics, machine learning, Generative AI, and agentic applications. You will develop 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.

You will work in a multi-cloud environment where Snowflake on AWS is the primary enterprise data platform and Google Cloud provides strategic AI, machine learning, agent, and advanced analytics capabilities.  As a senior individual contributor, you will influence enterprise technology decisions, establish reusable architecture patterns, guide complex initiatives, and mentor architects and engineers across the organization. You will also work hands-on with emerging technologies to rapidly experiment, build prototypes, and demonstrate the art of the possible for new data and AI capabilities.  

This role will have a hybrid work arrangement with 3 days a week in office in any of the following locations:
Charlotte, NC;
Hartford, CT;
Columbus, OH or Chicago, IL.  
*** Candidates must be eligible to work in the US without company sponsorship.***## ## 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…