Enterprise Data Architect
Core Specialty Insurance Holdings, Inc. · Dallas, TX · 1 mo ago
HybridEngineeringFull-time
We need to output HTML fragment with sections: use for headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to include sections only when posting has real specific content. The posting includes description (About the role) and responsibilities (Key Accountabilities/Deliverables). Also technical knowledge and understanding (maybe Skills). Experience (Qualifications). Benefits: At Core Specialty... includes salary, medical, dental, vision, life insurance, disability, 401k match, EAP, HSA, FSA, HRA, wellness program. Also "competitive salary". So Benefits section exists. Pay: "competitive salary" but not specific numbers; we should not create Pay section if only vague "competitive salary" (the instruction: never emit a heading followed by filler/placeholder text like "commensurate with experience", "not specified", "N/A", "varies", "TBD". "competitive salary" is vague but maybe considered filler? It's not a specific number. Likely we should not create Pay section because it's not specific. Schedule: not mentioned. So we omit Pay and Schedule.
We need sections: About the role (maybe we can use About the role heading). Responsibilities (Key Accountabilities/Deliverables). Requirements (maybe Experience). Qualifications (maybe Technical Knowledge and Understanding? Actually Technical Knowledge could be Skills). The instruction: Use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we need to map content accordingly.
We have:
- About the role: first paragraph describing role.
- Responsibilities: Key Accountabilities/Deliverables bullet list.
- Requirements: Experience section (Bachelor’s degree..., 15+ years..., etc.)
- Qualifications: maybe Technical Knowledge and Understanding? Or we could treat Technical Knowledge as Skills. But we need both Skills and Qualifications sections if content exists. Let's see: Technical Knowledge and Understanding includes deep understanding of patterns, hands-on knowledge of Azure, AI/ML architecture, DataOps, data governance, Snowflake, insurance domains, non-functional requirements, security architecture. That's more like Skills/Technical Qualifications. Could be Skills.
- Experience: we already used for Requirements. But we also have "Preferred certifications". That could be Qualifications? Actually Qualifications could include education, experience, certifications. But we already used Experience for Requirements. Might be better: Requirements = Experience (years, degree). Qualifications = Technical Knowledge and Understanding + Preferred certifications. Skills = maybe we could split but we have limited headings. Let's follow: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits.
We need to decide what goes where.
Let's extract content:
About the role: 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.
Responsibilities (Key Accountabilities/Deliverables): list items:
- 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.
Requirements (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.
Qualifications (Preferred certifications): 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. Also Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over work authorization sponsorship now or in the future for this position. (That's a requirement but maybe we can include in Qualifications as a note.)
Skills (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.
Benefits: 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.
We should not include Pay section because competitive salary is vague. Benefits section includes those details.
We need to output HTML fragment only, with
for headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to include sections only when posting has real specific content. The posting includes description (About the role) and responsibilities (Key Accountabilities/Deliverables). Also technical knowledge and understanding (maybe Skills). Experience (Qualifications). Benefits: At Core Specialty... includes salary, medical, dental, vision, life insurance, disability, 401k match, EAP, HSA, FSA, HRA, wellness program. Also "competitive salary". So Benefits section exists. Pay: "competitive salary" but not specific numbers; we should not create Pay section if only vague "competitive salary" (the instruction: never emit a heading followed by filler/placeholder text like "commensurate with experience", "not specified", "N/A", "varies", "TBD". "competitive salary" is vague but maybe considered filler? It's not a specific number. Likely we should not create Pay section because it's not specific. Schedule: not mentioned. So we omit Pay and Schedule.
We need sections: About the role (maybe we can use About the role heading). Responsibilities (Key Accountabilities/Deliverables). Requirements (maybe Experience). Qualifications (maybe Technical Knowledge and Understanding? Actually Technical Knowledge could be Skills). The instruction: Use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we need to map content accordingly.
We have:
- About the role: first paragraph describing role.
- Responsibilities: Key Accountabilities/Deliverables bullet list.
- Requirements: Experience section (Bachelor’s degree..., 15+ years..., etc.)
- Qualifications: maybe Technical Knowledge and Understanding? Or we could treat Technical Knowledge as Skills. But we need both Skills and Qualifications sections if content exists. Let's see: Technical Knowledge and Understanding includes deep understanding of patterns, hands-on knowledge of Azure, AI/ML architecture, DataOps, data governance, Snowflake, insurance domains, non-functional requirements, security architecture. That's more like Skills/Technical Qualifications. Could be Skills.
- Experience: we already used for Requirements. But we also have "Preferred certifications". That could be Qualifications? Actually Qualifications could include education, experience, certifications. But we already used Experience for Requirements. Might be better: Requirements = Experience (years, degree). Qualifications = Technical Knowledge and Understanding + Preferred certifications. Skills = maybe we could split but we have limited headings. Let's follow: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits.
We need to decide what goes where.
Let's extract content:
About the role: 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.
Responsibilities (Key Accountabilities/Deliverables): list items:
- 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.
Requirements (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.
Qualifications (Preferred certifications): 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. Also Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over work authorization sponsorship now or in the future for this position. (That's a requirement but maybe we can include in Qualifications as a note.)
Skills (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.
Benefits: 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.
We should not include Pay section because competitive salary is vague. Benefits section includes those details.
We need to output HTML fragment only, with headings and - for lists,
for prose.
We must not repeat job title as heading. Already not.
We must not include Job ID etc.
We must not include boilerplate like EEO etc. Already removed.
We need to preserve all substantive information. Ensure we don't lose any detail.
Let's craft:
About the role
... (the paragraph)
Responsibilities
- ... each item
...
Requirements
- 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
Qualifications
- 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
- for lists,
for prose. We must not repeat job title as heading. Already not. We must not include Job ID etc. We must not include boilerplate like EEO etc. Already removed. We need to preserve all substantive information. Ensure we don't lose any detail. Let's craft:
About the role
... (the paragraph)
Responsibilities
- ... each item ...
Requirements
- 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
Qualifications
- 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