Senior Data Analyst-Enterprise Data Modeller

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