StarHub
Senior Principal Data Architect
Senior data architecture authority for StarHub's DXP Data platform: owns target/reference architecture, domain data models, data contracts and governance-by-design across AWS, Snowflake, Airflow/Airbyte ingestion and SageMaker AI, guiding internal and vendor engineering teams from Singapore.
- Compensation
- Not disclosed
- Location
- SG
- Work setup
- Not specified
- Seniority
- Principal
- Employment
- Not specified
- Experience
- 7+ years
- Category
- Architecture
- Posted
Skills
Must have
- At least 7 years owning enterprise or domain data architecture and acting as 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 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, plus ingestion, orchestration, warehouse/lakehouse, open-table, metadata, lineage and self-service analytics
- Hands-on SQL and Python for data profiling, validation and prototyping
- Ability to embed security, privacy, classification, access control, quality, resilience, recoverability, observability, cost and regulatory requirements into architecture
- Practical understanding of AI/ML and agentic use cases, including AI-ready data, semantic grounding and responsible-AI controls
- Executive-level stakeholder leadership and leading through influence in vendor-augmented environments
Nice to have
- 15+ years of relevant experience (12+ years considered with equivalent principal-level scope)
- Airflow and ingestion technologies such as Airbyte, NiFi/Openflow or Glue
- Telecommunications or Customer 360 experience
Full description
Job DescriptionRole Mission:
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.