Senior Principal Data Architect

STARHUB LTD.; Singapore; On-site; SGD 140,000 - 210,000; Full time

Job summary

STARHUB LTD. is seeking a senior data architecture authority to translate business, analytics and AI priorities into a secure, scalable architecture across AWS, Snowflake and AI-enabled data use cases.

You will own the DXP Data target architecture, domain models and reusable data-product patterns to support C360 and self-service analytics. You will guide engineering and partner teams through reviews, prototypes and governance records, ensuring security, privacy, quality, cost awareness and

Qualifications

• 15+ years of relevant experience preferred, including 7+ years owning enterprise data architecture.
• Proven ownership of data-platform strategy and governance across multiple delivery teams.
• Deep expertise in conceptual, logical and physical data modelling.
• Cloud data-platform fluency with Snowflake and AWS.
• Hands-on SQL and Python credibility; experience with Airflow and ingestion technologies.

Responsibilities

• Own the DXP Data target and reference architecture.
• Lead conceptual and logical modelling; guide physical design and data-product boundaries.
• Approve material designs; manage architecture debt and ensure security, privacy, quality and lineage.
• Define reusable patterns across ingestion, storage, modelling, serving, integration, metadata and AI use cases.
• Guide engineering, analytics and vendor teams through reviews, prototypes, decision records and coaching.
• Translate stakeholder priorities into sequenced architecture outcomes and actionable decisions.

Skills

Data architecture

Cloud platforms

Leadership

SQL

Python

Open formats

Tools

Snowflake

AWS

Airflow

Airbyte

SageMaker

Job description

Serve as the senior data architecture authority for DXP Data, translating business, analytics and AI priorities into secure, scalable, operable and cost-aware architecture across AWS, Snowflake, ingestion, analytics and AI. Own target architecture, domain data models, reusable patterns and governance-by-design, and guide engineering teams and partners.

Accountabilities

• Architecture Strategy: Own the DXP Data target and reference architecture, principles, transition states and multi-year capability roadmap.
• Data Models and Products: Set direction for enterprise-aligned domain models, design semantic structures, data contracts and reusable data-product architecture supporting C360 and self-service analytics.
• Design Authority and Governance: Approve material designs, manage exceptions and architecture debt, and ensure security, privacy, quality, lineage, interoperability and operability are designed in.
• Standards and Paved Roads: Define and drive adoption of reusable patterns across ingestion, storage, modelling, serving, integration, metadata and AI-enabled data use cases.
• Business and Technology Alignment: Convert stakeholder priorities into sequenced architecture outcomes, explicit trade-offs and actionable decisions.
• Technical Leadership by Influence: Guide engineering, stewardship, analytics and vendor teams through reviews, prototypes, decision records, mentoring and knowledge transfer.

Responsibilities

• Architecture Roadmap: Maintain current, target and transition architectures; prioritize architecture runway, modernization, decommissioning and technical-debt reduction.
• Data Product Design: Lead conceptual and logical modelling, guide physical design, and define data-product boundaries, contracts, ownership, quality and service expectations.
• Reference Patterns: Define implementable patterns for batch, CDC, streaming and file ingestion; orchestration; warehouse / lakehouse design; sharing; metadata and lineage.
• Assurance and Operability: Review material designs and changes for correctness, security, resilience, scale, observability, cost and performance before build or release commitment.
• Technology Evolution: Evaluate and prototype platform choices across AWS, Snowflake, ingestion/orchestration, open formats, semantic or knowledge layers and AI; document evidence, TCO and risks.
• Stakeholder and Team Enablement: Facilitate decisions, challenge unclear requirements, assess partner designs and uplift internal capability through reusable guidance and coaching.

Qualifications

Team Scope / Stakeholders

• Scope: Data architecture and governance across the DXP Data Platform and C360 ecosystem, including AWS, Snowflake, Datapipe / Airflow/Airbyte, SageMaker, metadata, analytics enablement, and evolving data and AI platform capabilities.
• Decision Rights: Approval of target/reference architecture, strategic data and solution designs, enterprise/domain modelling patterns, data-product architecture, technology recommendations, architecture standards and exceptions; escalation of material security, quality, operational, cost, or architectural risks before delivery or go-live.
• Stakeholders: DXP Data Domain Owner, Enterprise and Solution Architecture, Data Engineering, Platform Engineering, Data Quality Stewards, BI/Analytics, Data Science/AI, Business Data Owners, Infrastructure, Cybersecurity/ISO, Legal/Risk, application domain teams, and strategic partners.
• Role Boundary / Resources: Senior individual-contributor and design-authority role operating across internal and vendor-augmented teams in Singapore, Malaysia and India. Engineering leads retain people leadership, implementation delivery and Day-2 operational ownership; the architect sets direction, assures design & operational quality, and drives cross-team alignment and capability uplift.

Minimum Profile/ Track Record

• 15+ years of relevant experience is preferred. Candidates with 12+ years may be considered where they demonstrate equivalent principal-level scope. Experience should include at least 7 years owning enterprise or domain data architecture and acting as a senior design authority in production-scale, multi-domain environments.
• Proven ownership of data-platform strategy, target and reference architecture, modernization roadmaps and architecture governance across internal and partner delivery teams.
• Deep expertise in conceptual, logical and physical data modelling, including customer/party models, dimensional and relational patterns, semantic layers, data products and data contracts.
• Strong technical fluency in cloud data-platform architecture - preferably Snowflake and AWS - and modern ingestion, orchestration, warehouse/lakehouse, open-table, metadata, lineage and self-service analytics patterns.
• Hands-on SQL and Python credibility sufficient to profile data, validate assumptions, prototype critical patterns and review implementation choices. Airflow and relevant ingestion technologies such