Senior Data Analyst-Enterprise Data Modeller

RAPSYS TECHNOLOGIES PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

RAPSYS TECHNOLOGIES PTE. LTD. in Singapore seeks an experienced Enterprise Data Modeler to lead the design of enterprise data models for banking and financial services domains.

The role focuses on conceptual, logical, and physical modelling, translating complex source data into standardised enterprise models and collaborating with global teams. Key capabilities include strong stakeholder engagement, data governance, and governance discipline, with exposure to MAS610 and regulatory reporting

Qualifications

• 6-8 years of enterprise data modelling experience.
• Strong hands-on experience developing conceptual, logical, and physical data models.
• Experience modelling data within finance, risk, regulatory reporting, or related banking domains.
• Strong understanding of banking data structures, business concepts, and data relationships.
• Experience with enterprise data modelling tools; ER/Studio experience is highly desirable.
• Exposure to banking or financial services data models and ability to adapt industry models to enterprise requirements.
• Experience supporting regulatory reporting and translating 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 translate them into standardised enterprise models.
• Strong stakeholder management and communication across business and technology teams.
• Experience in large, global organisations and across distributed teams.

Skills

Data modelling

Banking domain knowledge

Stakeholder management

Data governance

Data quality

Regulatory reporting

Data architecture

Data lineage

Tools

ER/Studio

Job description

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, business concepts, and data relationships.
• Experience working with enterprise data modelling tools; ER/Studio experience is highly desirable.
• Exposure to industry-standard banking or financial services data models and the ability to adapt industry models to enterprise requirements.
• Experience supporting regulatory reporting and translating 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 and translate them into standardised enterprise models.
• Strong stakeholder management and communication skills, with the ability to work effectively across business and technology teams.
• Experience working in large, complex, global organizations and across geographically distributed teams.

Preferred Qualifications

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