Banking Data Architect - BIAN and Data Governance

DiceTek UAE; Abu Dhabi; On-site; AED 480,000 - 660,000; Full time

Job summary

Dicetek LLC in Abu Dhabi is seeking an experienced Banking Data Architect to define enterprise data architecture for a large core banking transformation. You will develop banking data models, establish data governance, and align API/event structures with BIAN concepts.

The role emphasizes Master Data Management, domain modelling, metadata management and data quality, with governance of AI-assisted tooling to ensure validated outputs.

Qualifications

• 12-15 years of professional experience in banking data architecture.
• Experience on a large core banking transformation programme.
• Strong knowledge of MDM, data governance and metadata management.
• Familiarity with AI-assisted data modelling under governance.

Responsibilities

• Define banking data architecture principles for the core banking transformation.
• Develop and maintain banking domain data models.
• Define customer, account, product, transaction and party data models.
• Align enterprise data architecture with banking domain requirements.
• Review API and event payload structures for alignment with BIAN concepts.
• Coordinate data modelling with architecture and integration stakeholders.
• Establish data governance ownership and quality controls.
• Ensure AI-assisted outputs are governed and validated.

Skills

Banking data modelling

Master Data Management

Data governance

Data architecture

APIs & event design

BIAN concepts

Metadata management

Data lineage

Job description

Banking Data Architect – BIAN and Data Governance

Dicetek LLC is seeking an experienced Banking Data Architect to define enterprise and domain-level data architecture for a large-scale core banking transformation programme in Abu Dhabi, United Arab Emirates. The role focuses on developing consistent banking data models, establishing data governance controls and aligning API and event data structures with Banking Industry Architecture Network (BIAN) information concepts where appropriate. The successful candidate will bring extensive banking data architecture experience, with expertise in Master Data Management (MDM), canonical data modelling, banking domain models, metadata management, data lineage and data quality. A key responsibility is defining customer, account, product, transaction and party data models while supporting customer master integration and consistent information exchange across banking platforms. The position also involves applying governed AI-assisted tools to data modelling, mapping, metadata discovery and quality analysis, ensuring that AI-generated outputs remain subject to appropriate governance and validation controls.

Location: Abu Dhabi, United Arab Emirates

Country: United Arab Emirates

City: Abu Dhabi

Industry: Banking

Function: Database Development-Administration

Salary: Not disclosed

Gender: No Preference

Candidate Nationality: Not specified

Job Type: Full Time

Work Arrangement: On-site

Key Responsibilities

Banking Data Architecture and Modelling

• Define data architecture principles supporting the core banking transformation programme.
• Develop and maintain banking domain data models.
• Establish consistent definitions and relationships for critical banking data entities.
• Define customer, account, product, transaction and party data models.
• Support alignment between enterprise data architecture and banking domain requirements.
• Establish canonical and domain-specific data models.
• Provide architectural guidance on data structures and information exchange across banking systems.

BIAN Information Architecture

• Apply BIAN information concepts to relevant banking data architecture decisions.
• Align banking domain models with appropriate BIAN semantics.
• Support consistency between banking data entities and service-domain information requirements.
• Define relationships between domain data models and integration interfaces.
• Review API and event payload structures for alignment with relevant BIAN information concepts.
• Promote consistent banking data definitions across relevant platforms and services.

API Payload and Event Schema Design

• Define data architecture standards for API payloads.
• Establish consistent event schema structures supporting banking integration requirements.
• Align API and event data representations with relevant canonical and domain models.
• Review information structures used across banking service interfaces.
• Identify inconsistencies in data definitions and mappings.
• Support reusable and clearly documented data structures for integration services.
• Coordinate data modelling requirements with relevant architecture and integration stakeholders.

Master Data Management and Customer Master Integration

• Define data architecture considerations for Master Data Management.
• Guide integration of customer master data across relevant banking systems.
• Support consistent representation of customer and party information.
• Establish data modelling guidance for master data integration.
• Identify dependencies affecting the consistency of shared banking data.
• Coordinate relevant data architecture decisions across banking domains.
• Support alignment between MDM integration and established data governance requirements.

Data Governance and Ownership

• Establish data ownership principles for relevant banking data domains.
• Define governance controls supporting data consistency and accountability.
• Support documentation of data definitions and ownership responsibilities.
• Align data architecture decisions with relevant governance requirements.
• Promote consistent application of approved data models.
• Coordinate with relevant stakeholders on data governance considerations.
• Support governance review of significant data architecture decisions.

Data Lineage and Metadata Management

• Establish data lineage requirements across relevant banking platforms and integrations.
• Support documentation of data origins, transformations and dependencies.
• Define metadata requirements for banking data models and integration structures.
• Promote traceability between source data, canonical models and downstream information usage.
• Apply modern metadata management approaches to relevant architecture activities.
• Support identification of lineage gaps and documentation inconsistencies.
• Maintain alignment between metadata, data models and governance requirements.

Data Quality Architecture

• Define data quality controls for relevant banking data domains.
• Support consistent application of data quality requirements across data models and integrations.
• Identify potential data quality issues associated with inconsistent structures or mappings.
• Establish architecture considerations for data quality assessment.
• Coordinate data quality requirements with relevant stakeholders.
• Support analysis of data quality findings and associated improvement opportunities.
• Promote traceable and governed data quality practices.

AI-Assisted Data Architecture

• Apply AI-assisted tools to relevant data modelling activities.
• Use governed AI capabilities to support data mapping.
• Explore AI-assisted metadata discovery and documentation.
• Apply AI-supported analysis to identify potential data quality issues.
• Review AI-generated data models, mappings and recommendations for accuracy.
• Ensure AI-assisted activities follow applicable governance controls.
• Maintain appropriate human validation of AI-generated architecture outputs.

Required Experience

• 12-15 years of professional experience in banking data architecture.
• Experience working on a large core banking transformation programme.
• Strong knowledge of BIAN information concepts and banking domain models.
• Expertise in Master Data Management.
• Experience establishing data governance principles and controls.
• Strong understanding of API payload design.
• Experience defining event schemas for banking integration.
• Knowledge of data lineage and metadata management.
• Experience developing canonical and domain data models.
• Familiarity with modern data modelling tools.
• Experience with metadata and data quality platforms.
• Practical exposure to governed AI tools supporting data architecture activities.

Required Technical and Functional Skills

Banking Data Modelling

• Customer data modelling.
• Account data modelling.
• Product data modelling.
• Transaction data modelling.
• Party data modelling.
• Banking domain models.
• Canonical data models.
• Enterprise data architecture.

BIAN and Integration Data Architecture

• BIAN information concepts.
• Banking domain semantics.
• API payload design.
• Event schema design.
• Data mapping.
• Banking service information structures.
• Integration data consistency.

Master Data Management

• Master Data Management architecture.
• Customer master integration.
• Master data modelling.
• Shared banking data structures.
• Data ownership.
• Master data governance.

Data Governance and Quality

• Data governance controls.
• Data ownership frameworks.
• Data lineage.
• Metadata management.
• Data quality assessment.
• Data quality controls.
• Data traceability.

AI-Assisted Data Engineering Practices

• AI-assisted data modelling.
• AI-supported data mapping.
• AI-assisted metadata discovery.
• AI-enabled data quality analysis.
• Governed AI tool usage.
• Human validation of AI-generated outputs.

Technology and Tools

• Enterprise and domain data modelling.
• Master Data Management.
• Metadata management.
• Data lineage analysis.
• Data quality management.
• API payload and event schema design.
• Governed AI-assisted data architecture.

Specific software vendors and product versions are not prescribed in the supplied requirements.

Key Deliverables

Banking Data Models

• Defined customer, account, product, transaction and party data models.
• Established canonical data models.
• Documented banking domain data models.
• Consistent data definitions supporting core banking transformation requirements.

BIAN-Aligned Information Structures

• Data models aligned with relevant BIAN information concepts.
• API payload structures reflecting appropriate banking semantics.
• Event schemas supporting consistent information exchange.
• Documented relationships between domain models and integration interfaces.

Master Data Management Architecture

• Data architecture guidance for customer master integration.
• MDM integration considerations.
• Consistent master data definitions and mappings.
• Documented dependencies affecting shared banking information.

Data Governance and Quality Controls

• Defined data ownership requirements.
• Data lineage and metadata documentation.
• Data quality controls.
• Identified data consistency and traceability gaps.

AI-Assisted Architecture Improvements

• AI-assisted data modelling and mapping practices.
• Governed metadata discovery approaches.
• AI-supported data quality analysis.
• Validated AI-generated architecture outputs.

Professional Competencies

• Strong analytical and enterprise data architecture capabilities.
• Deep understanding of banking information structures and domain relationships.
• Ability to define consistent data models across complex banking environments.
• Strong knowledge of BIAN information concepts.
• Practical understanding of MDM and customer master integration.
• Ability to establish data governance, ownership and quality controls.
• Strong documentation and data modelling skills.
• Ability to coordinate data architecture requirements across relevant stakeholders.
• Sound judgment when evaluating data consistency and integration dependencies.
• Ability to use AI-assisted data architecture tools within appropriate governance boundaries.

Role Expectations

The Banking Data Architect will provide architectural direction for banking information models and data governance within a major core banking transformation programme. The successful candidate will define critical banking data entities, establish canonical and domain models, and guide alignment between API payloads, event schemas and relevant BIAN information concepts. The role requires strong expertise in Master Data Management, customer master integration, data ownership, lineage, metadata and data quality. Candidates should demonstrate 12-15 years of banking data architecture experience and the ability to apply modern modelling and governed AI-assisted tools to complex banking data requirements.