Data Modeller Wholesale Lending

Role Overview:

The Data Modeler – Wholesale Lending’ will play a critical role in designing, developing, and maintaining conceptual, logical, and physical data models supporting the Wholesale Lending Data Domain within the bank’s enterprise data ecosystem. The individual will collaborate closely with business SMEs, data architects, data engineers, and risk & regulatory teams to ensure the models meet business, analytical, and regulatory data needs, adhering to enterprise data standards and governance frameworks.

Key Responsibilities:

1. Data Modeling & Design

• Develop conceptual, logical, and physical data models for the Wholesale Lending domain/ applications/ platforms (e.g., Loans, Credit Facilities, Collateral, Exposure, Covenants, Customers, and Accounts).
• Develop semantic layers and canonical models to enable business-friendly data consumption. Maintain models in alignment with enterprise data standards, canonical models, and metadata repositories.
• Ensure consistency across reference data, master data, and transactional data within the lending lifecycle.
• Design models supporting Regulatory Reporting (Basel III/IV, IFRS9, BCBS 239), Credit Risk, and Exposure Management.
• Incorporate data lineage, quality, and classification annotations into the model. Create horizontal lineage across federated data structures for enterprise-wide integration.
• Ensure models adhere to industry standards (e.g., Accord, Lloyd’s CDR) and organizational principles.

2. Business Alignment & Data Domain Enablement

• Translate complex business requirements into data model structures that align with domain-driven design principles.
• Partner with Lending Operations, Credit Risk, Finance, and Regulatory Reporting stakeholders to capture data semantics and lineage.
• Support the definition of Data Products under the Wholesale Lending domain (e.g., Facility Data Product, Loan Exposure Product).
• Participate in the Data Domain Council to align with enterprise data strategies.

3. Technical Implementation Support

• Work with Data Engineers to ensure models are implemented accurately in data warehouses / data lakes / data mesh environments (e.g., Snowflake, Databricks, Teradata).
• Collaborate with ETL and integration teams to align source-to-target mappings with data models.
• Review and optimize data structures for performance and scalability in analytical and operational systems.

4. Data Governance & Quality

• Contribute to the data catalog, metadata management, and lineage documentation (e.g., using Collibra, Alation, Atlan, or Informatica EDC).
• Participate in data quality rule definition and validation aligned with the model entities.
• Support data stewardship processes by ensuring traceability from source systems (LOS, CRM, Core Banking) to domain entities.
• Enforce naming conventions, maintain data dictionaries, and ensure compliance with governance frameworks.
• Partner with enterprise architects to align models with data strategy and regulatory requirements.

5. Continuous Improvement & Innovation

• Recommend opportunities for model optimization, standardization, and reusability across domains (Retail, Treasury, Risk).
• Explore AI/ML-based metadata tagging and semantic model generation techniques for automation.
• Stay updated on industry data model standards (e.g., BIAN, ACORD, FIBO, ISO 20022).

Required Skills & Qualifications:

Technical Skills

• Strong expertise in data modeling tools (e.g., ER/Studio, ERwin, PowerDesigner).
• Deep understanding of relational, dimensional, and data vault modeling.
• Experience with cloud data platforms (Snowflake, Azure Synapse, GCP BigQuery, AWS Redshift, Databricks).
• Strong knowledge of SQL, PL/SQL, proficiency and ability to review data mappings.
• Familiarity with data governance tools (Collibra, Alation, Informatica, Atlan).
• Understanding of metadata management, lineage, and cataloging principles.

Domain Knowledge

• In-depth understanding of Wholesale / Corporate Lending lifecycle — origination, underwriting, credit approval, drawdown, servicing, collections, and closure.
• Exposure to credit risk, exposure management, and regulatory reporting data.
• Awareness of Basel III/IV, IFRS9, BCBS 239, and loan-level data models used by regulators.