Job Description

We are seeking a highly skilled

Enterprise Data Modeler

to design, develop, and maintain enterprise-level data models that support our banking operations, analytics, and regulatory compliance. The ideal candidate will have a deep understanding of financial services data structures, a strong grasp of data governance principles, and experience working in complex, regulated environments.

Key Responsibilities:

Design and maintain

conceptual, logical, and physical data models

across banking domains (e.g., Retail, Corporate, Risk, Compliance). Collaborate with business and IT teams to gather requirements and translate them into scalable and compliant data models. Ensure data models support

regulatory requirements

(e.g., BCBS 239, AML, KYC, Basel III) and

internal data governance

policies. Work closely with data architects, engineers, and analysts to implement models in data warehouses, data lakes, and operational systems. Drive standardization of data definitions and data quality metrics across the enterprise. Support

data lineage, metadata management

, and data cataloguing initiatives to enhance data transparency and traceability. Develop and maintain documentation of models, data dictionaries, and data flows. Assist in

M&A data integration

and system consolidation projects. Stay current with industry trends, data modelling tools, and regulatory changes affecting data structures.

Required Qualifications:

Bachelor's or master's degree in computer science, Information Systems, Data Science, or a related field. 5+ years of experience in

data modelling

, ideally within banking or financial services. Expertise in

data modelling tools

such as ERwin, IBM Infosphere Data Architect, or similar. Strong understanding of

relational, dimensional, and data vault modelling techniques.

Experience working with

data warehouse architectures

,

data lakes

, and cloud-based platforms (Azure). Solid knowledge of

banking systems

and data domains (e.g., customer, account, transaction, product, risk, compliance). Familiarity with

data governance frameworks

and regulatory compliance requirements (e.g., BCBS 239, GDPR). Strong SQL skills and understanding of database platforms (Oracle, SQL Server, Postgress, DB2). Datawarehouse design methodologies understanding.

Preferred Qualifications:

Experience with

master data management (MDM)

and metadata management tools. Knowledge of

real-time data modelling

for payments and fraud detection. * Exposure to

big data technologies

(e.g., Hadoop, Spark) and

streaming platforms

(Confluent).