Our client is a tier 1 bank, a globally recognised professional services institution operating across major financial markets. The organisation provides a broad range of services including assurance, advisory, tax, risk, regulatory consulting, transaction support, and technology-enabled transformation.

With a strong focus on innovation, data-driven decision-making, and regulatory excellence, the bank partners with corporations, governments, and financial institutions to manage risk, enhance performance, and navigate complex financial landscapes. Leveraging deep industry expertise and a global network of professionals, the organisation helps clients respond to evolving market conditions, digital disruption, and regulatory change.

Known for its rigorous standards, collaborative culture, and commitment to integrity, the bank invests heavily in technology, sustainability, and talent development to deliver long-term value for clients and stakeholders.

Role Requirements

• Develop conceptual and logical data models across key investment banking domains (e.g. clients, products, trades, positions, risk, finance, market and collateral data)
• Ensure models align to agreed modelling standards, naming conventions, and reusable design patterns
• Translate business requirements into clear, structured data models aligned to authoritative upstream sources
• Facilitate workshops with SMEs and data teams to validate definitions and resolve data discrepancies
• Embed modelling standards, metadata, and quality controls in line with enterprise data governance frameworks
• Operate within a high-throughput, iterative delivery (factory) model, supporting multiple concurrent data products
• Collaborate closely with architects, governance, engineering, onsite leadership, and offshore teams to ensure scalable and maintainable solutions
• Proven experience in conceptual/logical modelling within capital markets or investment banking
• Proficiency in data modelling tools (e.g. Hackolade, ERwin)
• Exposure to Data Vault methodology desirable

Required Experience

• Proven experience (typically 37+ years) in data modelling within financial services, banking, or large enterprise environments

• Strong expertise in conceptual, logical, and physical data modelling

• Hands-on experience with enterprise data modelling tools (e.g. ERwin, ER/Studio, PowerDesigner, or similar)

• Advanced SQL skills and strong understanding of relational database design (e.g. Oracle, SQL Server, PostgreSQL)

• Experience designing and optimising data models for:

• Data warehouses (Kimball/Inmon methodologies)

• Data marts

• Operational data stores (ODS)

Knowledge of dimensional modelling (star/snowflake schemas)Experience working with large-scale, complex, multi-source data environmentsUnderstanding of data governance, metadata management, and data lineage principlesFamiliarity with regulatory and risk data frameworks (e.g. BCBS 239) within financial services