JOB SUMMARY

The Director, Domain Architecture -- Data & AI is a horizontal enterprise architecture leader responsible for defining and governing the target-state architecture of the company's data, analytics, and artificial

intelligence ecosystem. This role owns the architectural strategy and standards for the Data & AI domain, including enterprise data platforms, canonical data models, event streaming, analytical and ML platforms, and the integration

patterns that enable data and insight consumption across digital, partner, shipboard, customer, and corporate systems. Operating within a federated architecture model under Product Engineering, this Director provides crosscutting

architectural leadership across vertical delivery teams and embedded Solution Architects. The role ensures architectural integrity, scalability, governance, and long-term sustainability of one of the company's most insights- and decision-critical technology domains.

This is a strategic architecture leadership role and does not function as an embedded delivery Solution Architect.

POSITION RESPONSIBILITIES

• Define and maintain the multi-year target-state architecture for the Data & AI domain, spanning data
• ingestion, storage, processing, analytics, and ML/AI enablement.
• Establish architectural guardrails for data platforms, AI/ML tooling, and self-service capabilities to
• balance agility with governance and control.
• Own domain capability models and architectural blueprints across:
  • Enterprise data lake / lakehouse and data warehouse platforms
  • Streaming and event-driven data pipelines
  • Master and reference data management, including canonical models for core entities (guest,
  • reservation, voyage, revenue, ship, itinerary, product)
  • BI, analytics, and reporting platforms
  • Data science and ML/AI platforms, feature stores, and model serving patterns
  • Data access, APIs, and semantic layers for consuming systems
  • Define domain-level non-functional requirements (data quality, timeliness, performance, scalability, availability, recoverability).
• Serve as the architectural authority for enterprise data platforms, analytical ecosystems, and ML/AI enablement platforms (vendor and custom).
• Partner with Data Governance, InfoSec, and Privacy functions to embed policies (security, privacy,
• lineage, retention) into platform and solution designs.
• Evaluate vendor roadmaps and influence platform strategy in partnership with product, analytics, and
• technology leadership.
• Provide architectural oversight for major data platform evolution, migrations, and new capability adoption (e.g., real-time streaming, generative AI, new cloud data services)
• Define Data & AI domain service contracts, data product interfaces, and API exposure standards.
• Establish clear data ownership boundaries and stewardship models for enterprise entities and events.
• Collaborate with:
  • Reservation Systems Architecture (booking, inventory, and transactional data streams)
  • Customer & Loyalty Architecture (guest profile, behavior, segmentation, loyalty accrual/redemption)
  • Shipboard Architecture (shipboard telemetry, operational, safety, and guest-experience data)
  • Corporate Applications Architecture (finance, HR, supply chain, and operational systems of record)
  • Enterprise Platform Architecture (cloud services, storage, compute, security, and observability foundations)
• Participate in cross-domain architecture decisions through the Enterprise Architecture Council.
• Define domain-level non-functional requirements (throughput, latency, cost-efficiency, RTO/RPO,
• elasticity) for data and AI platforms
• This role operates within a federated architecture model:
  • Enterprise / Domain Architecture (under Product Engineering) defines business capability architecture, domain boundaries, and application/data standards.
  • Enterprise Platform Architecture (under the CIO) defines infrastructure, cloud, network, DevOps, security, and hosting standards for data and AI workloads.
  • Solution Architects and Data/ML Architects are embedded in vertical engineering, analytics, and data science teams and design project-level solutions aligned to domain and platform standards.
• The Director, Domain Architecture -- Data & AI provides horizontal leadership and architectural authority across the enterprise for the Data & AI domain.

DEGREE TYPE:

Bachelor's Degree (Master's Preferred)

FIELD(S) OF STUDY:

Computer Science, Data Science, Artificial Intelligence, Information Systems, Statistics or Applied Mathematics

EXPERIENCE & QUALIFICATIONS

• 10–12+ years of progressive architecture experience in complex, data-intensive enterprise
• environments.
• Significant experience architecting enterprise data platforms, analytics ecosystems, and/or AI/ML
• solutions at scale.
• Deep knowledge of data architecture patterns (warehouse, data lake/lakehouse, streaming, data
• products) and their trade-offs.
• Experience with API-first and event-driven data integration patterns.
• Strong understanding of distributed systems, large-scale data processing, and non-functional design
• for data and ML workloads.
• Demonstrated ability to influence senior technology, analytics, and business leaders in a matrixed
• environment.
• Experience operating within federated architecture and data governance models.
• Consumer-centric industry experience with rich customer and operational data.
• Experience with modern cloud data platforms and ML/AI tooling (in partnership with infrastructure
• and data platform teams).
• Familiarity with CRM, loyalty, digital, and operational data integration patterns, including identity
• resolution and segmentation.
• Experience defining and governing MLOps, model lifecycle, and responsible AI practices.
• Experience managing vendor lifecycle strategy and major data/AI platform upgrades or migrations

COMPETENCIES & SKILLS

• Strategic systems thinker with strong data and business acumen.
• Executive presence and ability to communicate complex data and AI architecture trade-offs in clear
• business terms.
• Governance-oriented but innovation-enabling mindset, balancing experimentation with control.
• Ability to influence without direct authority across technology, analytics, and business domains.
• Strong risk management discipline for data privacy, security, compliance, and AI ethics.