Enterprise Data Modeller

Tap Growth ai; Singapore; On-site; SGD 120,000 - 170,000; Full time

Job summary

Tap Growth ai is seeking an experienced Enterprise Data Modeller in Singapore to enhance data architecture for business needs. You will craft enterprise data models, align with banking data structures, and collaborate with global teams to translate requirements into scalable designs.

Responsibilities emphasize developing conceptual, logical and physical models, supporting regulatory reporting, and leveraging tools like ER/Studio to govern metadata and data quality across large data environments.

Qualifications

• 6–8 years of enterprise data modelling experience in banking or financial services.
• Strong hands-on experience delivering conceptual, logical and physical data models.
• Experience modelling data within finance, risk or regulatory domains.
• Understanding banking data structures, concepts and data relationships.
• Experience with enterprise modelling tools (ER/Studio preferred).
• Exposure to standard banking data models and enterprise integration.
• Ability to translate regulatory requirements into data models.
• Knowledge of data governance, metadata, lineage and quality principles.
• Ability to analyse complex source systems and produce standardised models.
• Strong stakeholder management and cross-functional collaboration.

Responsibilities

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Skills

Enterprise data modelling

Conceptual/Logical/Physical models

Banking/finance domain knowledge

Data structures & relationships

Regulatory reporting knowledge

Stakeholder management

Data governance & metadata

Data quality & lineage

Analytical problem solving

Distributed teams experience

Tools

ER/Studio

Job description

We're Hiring: Enterprise Data Modeller!

We are looking for a highly skilled and experienced Enterprise Data Modeller to join our team in Singapore. The ideal candidate will have a strong background in data modeling, database design, and analytics with a proven track record of enhancing data architecture to meet business needs.

Location:

Singapore, Singapore

Work Mode:

Work from Office

Role:

Enterprise Data Modeller

What You'll Do:

Required Experience and Skills

• 6–8 years of enterprise data modelling experience, preferably within banking or financial services.
• Strong hands‑on experience developing conceptual, logical,and physical data models.
• Demonstrated experience modelling data within finance, risk,regulatory reporting, or related banking domains.
• Strong understanding of banking data structures, businessconcepts, and data relationships.
• Experience working with enterprise data modelling tools; ER/Studioexperience is highly desirable.
• Exposure to industry-standard banking or financial servicesdata models and the ability to adapt industry models to enterpriserequirements.
• Experience supporting regulatory reporting andtranslating regulatory or reporting requirements into data models.
• Knowledge of enterprise data architecture, data governance,metadata management, data lineage, and data quality principles.
• Ability to analyse complex source systems and datasets andtranslate them into standardised enterprise models.
• Strong stakeholder management and communication skills, withthe ability to work effectively across business and technology teams.
• Experience working in large, complex, global organisations andacross geographically distributed teams.

Preferred Qualifications

• Experience with banking regulatory reporting frameworks,including exposure to MAS610.
• Experience establishing or implementing enterprise-wide datamodelling standards and governance.
• Experience with Silver-layer, curated, canonical, or enterprisedata models within modern data platforms.
• Familiarity with data warehouse, data lake, lakehouse, andanalytical data architectures.
• Exposure to large-scale banking data transformation orregulatory change programmes.
• Experience contributing to global data architecture or datamanagement initiatives.

Key Competencies

• Enterprise and strategic thinking
• Strong analytical and data modelling capability
• Banking, finance, and risk domain knowledge
• Attention to detail and data quality
• Stakeholder engagement and facilitation
• Ability to simplify complex data requirements
• Strong documentation and governance discipline
• Collaborative approach across global and cross-functional teams