The Enterprise Data Architect is responsible for defining and governing the target-state enterprise data architecture across operational, analytical, AI/ML, and reporting platforms. This role partners with business, engineering, security, and governance stakeholders to establish scalable data patterns, trusted data products, AI-ready data foundations, and resilient data operations that support underwriting, claims, finance, risk aggregation, regulatory reporting, and enterprise analytics.
The role will provide architecture leadership across cloud-native data platforms, integration patterns, data governance, DataOps/MLOps, and engineering standards. The architect will ensure solutions are secure, compliant, observable, automated, reusable, and aligned to enterprise architecture guardrails and insurance industry expectations.
Key Accountabilities/Deliverables:- Define the enterprise data architecture strategy, reference patterns, roadmap, and standards across data ingestion, transformation, storage, consumption, AI/ML, and operational reporting capabilities.
- Establish target-state architectures for data platforms including Lakehouse, data warehouse, semantic layer, data mesh/domain-aligned data products, master/reference data, metadata, lineage, cataloging, and data quality management.
- Partner with business and technology leaders to 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 governed feature stores, vector/embedding patterns, model training and inference data pipelines, retrieval-augmented generation grounding, and responsible AI controls.
- Lead architecture reviews for data and analytics initiatives, ensuring alignment to security, privacy, regulatory, data classification, retention, least privilege, segregation of duties, and audit readiness requirements.
- Define DataOps, MLOps, and engineering requirements for CI/CD, automated 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 reusable implementation playbooks for engineering teams.
- Guide modernization of legacy data assets and reporting solutions into cloud-native, secure, scalable, and cost-optimized platforms aligned to Azure-first enterprise direction with limited AWS workloads where appropriate.
- Support vendor/platform evaluations using build vs. buy vs. extend analysis, ensuring selections align to enterprise architecture, integration, security, compliance, extensibility, and total cost of ownership.
- Partner with cybersecurity and platform teams to implement Zero Trust data access, network segmentation, encryption, key management, privileged access controls, and secure data sharing patterns.
- Drive operational excellence by defining observability standards for pipelines, data products, models, SLAs/SLOs, lineage, incident response, DR/BCP, capacity, cost management, and service health reporting.
Technical Knowledge and Understanding:- Deep understanding of enterprise data architecture patterns including Lakehouse, data warehouse, data vault, medallion architectures, data mesh, domain-driven design, canonical data models, event-driven integration, APIs, and batch/streaming ingestion.
- Hands-on knowledge of cloud-native data platforms and services, preferably Microsoft Azure including Microsoft Fabric, Synapse, ADLS Gen2, Azure SQL, Data Factory/Synapse Pipelines, Azure Functions, Event Hubs, Databricks, Power BI, Purview, Key Vault, Monitor, Log Analytics, and Sentinel integrations.
- Strong understanding of AI/ML architecture including model lifecycle, supervised/unsupervised learning concepts, feature engineering, prompt grounding, vector stores, LLM/RAG solution patterns, Copilot/agent architectures, responsible AI, model risk, and hallucination mitigation.
- Strong DataOps and engineering practices including Git branching, CI/CD pipelines, automated testing, schema validation, data quality gates, contract testing, reusable frameworks, IaC, containers/serverless, and secure DevSecOps practices.
- Expertise in data governance capabilities including data catalog, lineage, classification, retention, privacy controls, stewardship workflows, metadata management, reference/master data, and data quality measurement.
- Working knowledge of Snowflake and hybrid data platform patterns, including cross-platform governance, data sharing, workload placement, cost controls, and integration with enterprise BI and AI/ML use cases.
- Understanding of insurance data domains and operational needs such as policy, billing, claims, producers, insureds, coverages, exposures, risk, loss, finance, regulatory reporting, and delegated authority data flows.
- Ability to define non-functional requirements for performance, scalability, high availability, disaster recovery, latency, observability, data freshness, data retention, operational support, and cost optimization.
- Knowledge of security architecture for data platforms including Zero Trust, least privilege RBAC/ABAC, encryption at rest/in transit, private endpoints, secrets management, DLP, conditional access, privileged access, audit logging, and secure file transfer patterns.
- Other duties as assigned.
Experience:- 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; insurance or financial services experience preferred.
- Strong communication skills with the ability to convert complex technical concepts into executive-ready recommendations, roadmaps, trade-off analyses, and delivery guidance.
- 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.
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At Core Specialty, you will receive a competitive salary and opportunities for professional development and advancement. We offer medical, dental, vision, and life insurances; short and long-term disability; a Company-match of 100% of a 6% contribution 401(k) plan; an Employee Assistance Plan; Health Savings Account, Flexible Spending Account, Health Reimbursement Account, and a wellness program
