Data Architecture Manager
Norwegian Cruise Line Holdings Ltd., Miami, FL, United States
The Manager, Data Architecture leads the design, governance, and evolution of the enterprise data architecture strategy within a modern cloud-native ecosystem. This role is responsible for managing architectural standards, guiding data engineering teams, overseeing platform design (e.g., Snowflake, dbt), and ensuring that data systems are scalable, secure, AI-ready, and aligned with enterprise governance. The position drives the transformation from traditional warehouse-centric design toward domain-oriented data products, automation, and advanced analytics enablement.
REQUIREMENTS
Define and govern enterprise-wide data architecture standards, patterns, and best practices across cloud platforms.
Lead architectural decisions across Snowflake, ELT/dbt frameworks, CI/CD pipelines, observability tooling, and security models.
Ensure architectural consistency across ingestion, transformation, semantic, and consumption layers.
Data Product & Domain Architecture Strategy
Establish scalable domain-driven data product patterns that promote ownership, reuse, and SLA-based delivery.
Drive logical and physical data modeling standards (dimensional, data vault, semantic layer design).
Partner with business and analytics teams to ensure architectures support self-service analytics and enterprise reporting needs.
AI & Advanced Analytics Enablement
Ensure data platforms are architected to support AI/ML workloads, including structured and unstructured data integration.
Enable LLM‑ready architectures, metadata enrichment, vectorization strategies, and real‑time/event‑driven use cases.
Support Data Science and advanced analytics initiatives through optimized pipeline and storage design.
Governance, Security & Data Quality Oversight
Embed data quality frameworks, automated testing, observability, lineage, and monitoring into architecture standards.
Ensure compliance with enterprise governance, security, privacy, and regulatory requirements (e.g., SOX, PCI where applicable).
Partner with DataOps and DevOps to operationalize governance and CI/CD controls.
Promote a culture of engineering excellence, automation, and continuous improvement.
Lead, mentor, and develop data architects and senior data engineers.
Establish architectural review processes and technical design governance forums.
Promote a culture of engineering excellence, automation, and continuous improvement.
Partner with external vendors and consulting partners to align implementations with enterprise architecture standards.
Collaborate cross‑functionally with Engineering, Martech, Data Science, DevOps, Security, and Product teams.
Serve as the primary escalation point for complex architectural decisions.
Strategic Planning & Continuous Modernization
Contribute to long‑term roadmap planning for platform scalability, modernization, and cost optimization.
Evaluate emerging technologies and assess adoption value within the enterprise ecosystem.
Drive modernization of legacy platforms where strategically appropriate.
Perform other job‑related functions as assigned.
QUALIFICATIONS
DEGREE TYPE
Bachelor's Degree
FIELD(S) OF STUDY
Business Management, Computer Science, Industrial Engineering, or a related field; or an equivalent combination of relevant work experience and education.
EXPERIENCE
8–12+ years of progressive experience in data architecture, data engineering, or enterprise data platform leadership roles.
Proven experience designing and governing cloud‑native data platforms (e.g., Snowflake, dbt, modern ELT architectures).
Experience leading technical teams and managing architectural standards in Agile product environments.
Strong expertise in advanced data modeling techniques and enterprise data product design.
Experience implementing CI/CD, DevOps/DataOps practices, and observability within data ecosystems.
Experience enabling AI/ML and advanced analytics workloads, including real‑time or event‑driven architectures.
Demonstrated experience in modernizing legacy data systems and guiding large‑scale architectural transitions.
COMPETENCIES & SKILLS
Deep understanding of modern data architecture principles, cloud ecosystems, and distributed data platforms.
Strong leadership and coaching capabilities with ability to influence across technical and business teams.
Advanced proficiency in SQL and working knowledge of Python or similar languages for automation and extensibility.
Strong understanding of data governance, metadata management, lineage, and quality frameworks.
Experience architecting secure, scalable, and cost‑optimized cloud solutions.
Excellent analytical, communication, and executive presentation skills.
Ability to balance innovation with operational stability and enterprise standards.
CERTIFICATIONS & LICENSES
Cloud data platform certifications (e.g., Snowflake, AWS, Azure, or GCP) preferred.
dbt, data engineering, or analytics engineering certifications are a plus.
AI/ML or data governance certifications (e.g., DAMA/CDMP, SnowPro Advanced) are desirable but not required.
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REQUIREMENTS
Define and govern enterprise-wide data architecture standards, patterns, and best practices across cloud platforms.
Lead architectural decisions across Snowflake, ELT/dbt frameworks, CI/CD pipelines, observability tooling, and security models.
Ensure architectural consistency across ingestion, transformation, semantic, and consumption layers.
Data Product & Domain Architecture Strategy
Establish scalable domain-driven data product patterns that promote ownership, reuse, and SLA-based delivery.
Drive logical and physical data modeling standards (dimensional, data vault, semantic layer design).
Partner with business and analytics teams to ensure architectures support self-service analytics and enterprise reporting needs.
AI & Advanced Analytics Enablement
Ensure data platforms are architected to support AI/ML workloads, including structured and unstructured data integration.
Enable LLM‑ready architectures, metadata enrichment, vectorization strategies, and real‑time/event‑driven use cases.
Support Data Science and advanced analytics initiatives through optimized pipeline and storage design.
Governance, Security & Data Quality Oversight
Embed data quality frameworks, automated testing, observability, lineage, and monitoring into architecture standards.
Ensure compliance with enterprise governance, security, privacy, and regulatory requirements (e.g., SOX, PCI where applicable).
Partner with DataOps and DevOps to operationalize governance and CI/CD controls.
Promote a culture of engineering excellence, automation, and continuous improvement.
Lead, mentor, and develop data architects and senior data engineers.
Establish architectural review processes and technical design governance forums.
Promote a culture of engineering excellence, automation, and continuous improvement.
Partner with external vendors and consulting partners to align implementations with enterprise architecture standards.
Collaborate cross‑functionally with Engineering, Martech, Data Science, DevOps, Security, and Product teams.
Serve as the primary escalation point for complex architectural decisions.
Strategic Planning & Continuous Modernization
Contribute to long‑term roadmap planning for platform scalability, modernization, and cost optimization.
Evaluate emerging technologies and assess adoption value within the enterprise ecosystem.
Drive modernization of legacy platforms where strategically appropriate.
Perform other job‑related functions as assigned.
QUALIFICATIONS
DEGREE TYPE
Bachelor's Degree
FIELD(S) OF STUDY
Business Management, Computer Science, Industrial Engineering, or a related field; or an equivalent combination of relevant work experience and education.
EXPERIENCE
8–12+ years of progressive experience in data architecture, data engineering, or enterprise data platform leadership roles.
Proven experience designing and governing cloud‑native data platforms (e.g., Snowflake, dbt, modern ELT architectures).
Experience leading technical teams and managing architectural standards in Agile product environments.
Strong expertise in advanced data modeling techniques and enterprise data product design.
Experience implementing CI/CD, DevOps/DataOps practices, and observability within data ecosystems.
Experience enabling AI/ML and advanced analytics workloads, including real‑time or event‑driven architectures.
Demonstrated experience in modernizing legacy data systems and guiding large‑scale architectural transitions.
COMPETENCIES & SKILLS
Deep understanding of modern data architecture principles, cloud ecosystems, and distributed data platforms.
Strong leadership and coaching capabilities with ability to influence across technical and business teams.
Advanced proficiency in SQL and working knowledge of Python or similar languages for automation and extensibility.
Strong understanding of data governance, metadata management, lineage, and quality frameworks.
Experience architecting secure, scalable, and cost‑optimized cloud solutions.
Excellent analytical, communication, and executive presentation skills.
Ability to balance innovation with operational stability and enterprise standards.
CERTIFICATIONS & LICENSES
Cloud data platform certifications (e.g., Snowflake, AWS, Azure, or GCP) preferred.
dbt, data engineering, or analytics engineering certifications are a plus.
AI/ML or data governance certifications (e.g., DAMA/CDMP, SnowPro Advanced) are desirable but not required.
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