Principal Data, AI Architect

The Hartford

. Define line-of-business and cross-domain data architectures aligned with enterprise data, AI, cloud, and technology strategies .

Posted 10/26/2026full-timeUnited StatesLead💰 $140,000 - $210,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and implementing enterprise data architectures, integrating cloud services, and ensuring data governance and quality. Proficient in leveraging advanced analytics, AI, and machine learning capabilities to drive business insights and decision-making.

Highest-signal resume keywords

Enterprise Data ArchitectureSnowflakeGoogle Cloud AI ServicesData GovernanceMLOps

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ArchitectureData EngineeringSQLPythonPySparkApache IcebergSemantic ModelingData IntegrationAnalytics ArchitectureGenerative AI

Soft Skills

CommunicationPresentationFacilitationInfluencing

Tools & Technologies

AWSBigQueryDataflowKnowledge CatalogGemini Enterprise Agent PlatformManaged SparkCloud Object StorageGraph Platforms

Certifications & Qualifications

Snowflake CertificationAWS CertificationGoogle Cloud CertificationData Architecture CertificationData Engineering Certification

Industry Keywords

Regulated EnvironmentsProperty and Casualty InsuranceEmployee BenefitsFinancial Services

Tech Stack

Tools & technologies

ApacheAWSBigQueryCloudCyber SecurityPySparkPythonSnowflakeSparkSQL

About the role

Key responsibilities & impact

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 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
Architect capabilities for conversational and agentic analytics, including natural-language data access, proactive insight generation, anomaly analysis, root-cause analysis, and decision support
Establish secure and cost-effective patterns integrating Snowflake on AWS with Google Cloud AI and analytics services
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 and 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
Lead architecture reviews, assess emerging technologies, perform hands-on experiments and rapid prototyping, 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 and contribute to architecture forums and communities of practice

Requirements

What you’ll need

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
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
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
Proficiency with SQL and experience with Python, PySpark, Snowpark, or comparable data-engineering technologies
Excellent communication, presentation, facilitation, and influencing skills
Professional certifications in Snowflake, AWS, Google Cloud, data architecture, or data engineering (preferred)
Experience in property and casualty insurance, employee benefits, financial services, or another regulated industry (preferred)

Benefits

Comp & perks

Short-term or annual bonuses
Long-term incentives
On-the-spot recognition
Hybrid work arrangement