Enterprise Data Architect

Core Specialty Insurance Holdings, Inc.

. Define the enterprise data architecture strategy, reference patterns, roadmap, and standards across ingestion, transformation, storage, consumption, AI/ML, and operational reporting .

Posted 9/16/2026full-timeCincinnati • Ohio • United StatesSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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

Expertise in enterprise data architecture, cloud-based data platforms, and AI/ML enablement, with a strong focus on data governance, quality management, and compliance in regulated environments. Proven ability to lead complex data transformation initiatives and communicate technical concepts effectively to stakeholders.

Highest-signal resume keywords

Enterprise Data ArchitectureCloud-Based Data PlatformsAI/ML EnablementData GovernanceData Quality Management

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

SQLPythonData ModelingPipeline DesignAPIs/Integration PatternsAutomated TestingProduction SupportMLOps PatternsData CatalogingMetadata-Driven Governance

Soft Skills

Strong Communication Skills

Tools & Technologies

AzureMicrosoft FabricData Flow DiagramsData MeshFeature Stores

Certifications & Qualifications

Azure Solutions Architect ExpertAzure Data Engineer AssociateMicrosoft Fabric Analytics EngineerDP-900AI-900SnowPro

Industry Keywords

InsuranceFinancial ServicesRegulatory ComplianceData TransformationData Governance

Tech Stack

Tools & technologies

AzureCloudCyber SecurityPySparkPythonSQL

About the role

Key responsibilities & impact

Define the enterprise data architecture strategy, reference patterns, roadmap, and standards across ingestion, transformation, storage, consumption, AI/ML, and operational reporting
Establish target-state architectures for Lakehouse, data warehouse, semantic layer, data mesh/domain-aligned data products, master/reference data, metadata, lineage, cataloging, and data quality management
Translate underwriting, claims, finance, actuarial, risk, and regulatory needs into governed data capabilities and reusable engineering patterns
Design and govern AI-ready data foundations, including feature stores, vector/embedding patterns, model training and inference pipelines, RAG grounding, and responsible AI controls
Lead architecture reviews for data and analytics initiatives, ensuring security, privacy, regulatory, classification, retention, least privilege, segregation of duties, and audit readiness
Define DataOps, MLOps, and engineering requirements for CI/CD, testing, data quality gates, policy-as-code, infrastructure-as-code, environment promotion, rollback, monitoring, and release controls
Create architecture blueprints, solution decision records, integration patterns, data flow diagrams, domain models, canonical data contracts, and implementation playbooks
Guide modernization of legacy data assets and reporting solutions into secure, scalable, cost-optimized cloud-native platforms aligned to Azure-first direction
Support vendor and platform evaluations through build-vs-buy-vs-extend analysis
Partner with cybersecurity and platform teams on Zero Trust access, network segmentation, encryption, key management, privileged access, and secure data sharing
Define observability standards for pipelines, data products, models, SLAs/SLOs, lineage, incident response, DR/BCP, capacity, cost management, and service health reporting
Perform other duties as assigned

Requirements

What you’ll need

Bachelor’s degree or equivalent work experience
15+ years of progressive experience in enterprise data architecture, data engineering, analytics, or related technology leadership roles
5+ years designing or governing cloud-based data platforms and enterprise-scale analytics solutions
Demonstrated experience leading architecture for complex data transformation, modernization, governance, or AI/ML enablement initiatives across business and IT stakeholders
Hands-on engineering credibility with SQL, Python or PySpark, data modeling, pipeline design, APIs/integration patterns, Git-based delivery, automated testing, and production support practices
Experience with BI/semantic modeling, data quality management, master/reference data management, data cataloging, lineage, and metadata-driven governance
Experience defining MLOps patterns for model registration, experiment tracking, model validation, deployment, monitoring, drift detection, retraining workflows, human-in-the-loop controls, and production support
Proven ability to define reference architectures, standards, data patterns, technical guardrails, solution blueprints, and architecture decision records for engineering teams
Experience partnering with security, risk, compliance, audit, legal, and privacy stakeholders to design governed data and AI solutions in regulated environments
Strong communication skills with the ability to convert complex technical concepts into executive-ready recommendations, roadmaps, trade-off analyses, and delivery guidance
Insurance or financial services experience preferred
Preferred certifications: Azure Solutions Architect Expert, Azure Data Engineer Associate, Microsoft Fabric Analytics Engineer, DP-900/AI-900, SnowPro, or equivalent cloud/data/AI certifications
Applicants must be authorized to work for any employer in the U.S.
Employer cannot sponsor or take over work authorization sponsorship now or in the future

Benefits

Comp & perks

Competitive salary
Opportunities for professional development and advancement
Medical insurance
Dental insurance
Vision insurance
Life insurance
Short-term disability
Long-term disability
Company-match of 100% of a 6% contribution 401(k) plan
Employee Assistance Plan
Health Savings Account
Flexible Spending Account
Health Reimbursement Account
Wellness program