Senior Data Modeling & Analytics Architect

RECRUIT EXPRESS PTE LTD

Singapore

On-site

SGD 110,000 - 150,000

Full time

14 days+

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Job summary

RECRUIT EXPRESS PTE LTD in Singapore is seeking a Senior Data Modeller to design and maintain enterprise data models across domains such as Customer, Policy, Product, Distribution, Claims, Finance and Operations, aligning with analytics objectives.

You will collaborate with business users, solution architects and data engineers to translate requirements into scalable models, develop semantic layers in Snowflake and produce data mappings, glossaries and metadata, enabling self-service analytics

Qualifications

Experience in Data Modelling, Analytics Engineering, Semantic Layer Development or related data discipline.
Strong understanding of conceptual, logical and physical data modelling principles.
Experience designing semantic models, including business entities, dimensions, measures, hierarchies and reusable metrics.
Ability to translate business terminology and reporting requirements into scalable data models and implementation-ready specifications.
Strong SQL skills with experience on large-scale data platforms.
Experience producing ER diagrams, business glossaries, metadata documentation and mappings.
Strong communication skills bridging business and technical stakeholders.
Experience in the financial services or insurance industry is advantageous.

Responsibilities

Design, maintain and enhance enterprise data models across key business domains (Customer, Policy, Product, Distribution, Claims, Finance and Operations).
Develop conceptual, logical and physical data models aligned with analytics objectives.
Identify opportunities to improve data model coverage across operational and analytical platforms.
Define consistent business entities, attributes, relationships and standard definitions.
Partner with business users, solution architects and data engineers to translate requirements into scalable data models.
Design and maintain semantic models that support self-service analytics, reporting and AI-enabled use cases.
Develop business-friendly semantic layers and curated data structures within Snowflake.
Define reusable metrics, dimensions, hierarchies and measures to promote enterprise-wide consistency.
Support AI and GenAI initiatives by establishing clear business terminology and definitions.
Collaborate with data engineering teams to translate models into implementation-ready specifications.
Produce data mapping documents, glossaries, metadata definitions, lineage documentation and ER diagrams.
Onboard and integrate new data sources into the enterprise data model.
Review data structures for alignment with enterprise data modelling standards.
Work with business and technology teams to define enterprise data concepts.