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Data Modeler - Job Opportunity at Dicetek LLC

Dubai, AE
Full-time
Senior
Posted: June 3, 2025
On-site
AED 180,000 - 280,000 per year (USD 49,000 - 76,000). Dubai's financial services sector offers competitive compensation for senior data modeling roles, with packages often including housing allowances, health benefits, and tax advantages. The salary range reflects the specialized nature of banking data modeling and the region's competitive talent market.

Key Responsibilities

Architect and maintain enterprise-wide conceptual, logical, and physical data models spanning critical banking domains including retail banking, corporate banking, risk management, and regulatory compliance, ensuring these models serve as the foundational blueprint for data-driven decision making across the organization
Lead cross-functional collaboration initiatives with business stakeholders and IT teams to gather complex requirements and translate them into scalable, compliant data architectures that support both current operations and future growth objectives
Ensure comprehensive data model compliance with stringent regulatory frameworks including BCBS 239, Anti-Money Laundering (AML), Know Your Customer (KYC), and Basel III requirements while maintaining alignment with internal data governance policies and industry best practices
Partner strategically with data architects, engineers, and analysts to implement robust data models across diverse technological environments including data warehouses, data lakes, and operational systems, ensuring seamless integration and optimal performance
Drive enterprise-wide standardization of data definitions and establish comprehensive data quality metrics that enhance organizational data consistency, reliability, and usability across all business units
Champion data lineage initiatives, metadata management programs, and data cataloguing projects to significantly enhance data transparency, traceability, and accessibility for enterprise-wide data consumers
Develop and maintain comprehensive documentation including detailed data models, data dictionaries, and data flow diagrams that serve as critical reference materials for ongoing operations and future development initiatives
Provide expert support for complex mergers and acquisitions data integration projects and system consolidation initiatives, ensuring smooth data migration and minimal business disruption
Maintain cutting-edge expertise in industry trends, advanced data modelling tools, and evolving regulatory changes to ensure organizational data structures remain competitive and compliant in the dynamic financial services landscape

Requirements

Education

Bachelor's or master's degree in computer science, Information Systems, Data Science, or a related field

Experience

5+ years of experience in data modelling, ideally within banking or financial services

Required Skills

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 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)
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Sauge AI Market Intelligence

Industry Trends

The financial services industry is experiencing unprecedented digital transformation, with banks investing heavily in cloud-native data architectures and real-time analytics capabilities to compete with fintech disruptors and meet evolving customer expectations for instant, personalized services. Regulatory compliance requirements are becoming increasingly complex and data-centric, with frameworks like BCBS 239 and Basel III requiring banks to maintain robust data lineage, governance, and reporting capabilities that can only be achieved through sophisticated enterprise data modeling. The Middle East banking sector, particularly in the UAE, is rapidly adopting advanced data management practices as part of national digitization initiatives and to support the region's position as a global financial hub, creating significant demand for experienced data modeling professionals. Data vault modeling and cloud-based data lake architectures are becoming industry standards, replacing traditional data warehouse approaches as banks seek more flexible, scalable solutions to handle diverse data types and real-time processing requirements.

Role Significance

Typically works within a data architecture team of 5-8 professionals, collaborating closely with 2-3 other data modelers while interfacing with larger cross-functional teams including 10-15 data engineers, business analysts, and IT architects across various banking domains.
This is a senior-level individual contributor role with significant enterprise-wide impact and influence. The position requires deep expertise and independent decision-making capabilities, with responsibilities spanning across multiple business domains and requiring interaction with executive-level stakeholders on strategic data initiatives.

Key Projects

Enterprise data warehouse modernization initiatives involving migration from legacy systems to cloud-based architectures Regulatory compliance data model development for BCBS 239 and Basel III reporting requirements Master data management implementation projects for customer, product, and risk data domains Real-time fraud detection and payment processing data model optimization Merger and acquisition data integration projects requiring complex system consolidation and data harmonization

Success Factors

Deep understanding of banking business processes and regulatory requirements is essential, as data models must accurately reflect complex financial instruments, risk calculations, and compliance reporting needs that are unique to the banking industry. Strong communication and stakeholder management skills are critical for translating business requirements into technical data structures while managing expectations and ensuring buy-in from both technical and business teams across the organization. Expertise in both traditional and modern data modeling approaches is necessary to balance existing system constraints with innovative solutions, requiring proficiency in relational, dimensional, and data vault methodologies. Regulatory knowledge and compliance mindset are fundamental, as errors in data modeling can result in significant regulatory penalties and reputational damage for financial institutions. Adaptability and continuous learning are essential given the rapidly evolving technology landscape and changing regulatory environment in financial services.

Market Demand

High demand with limited supply of qualified candidates. The combination of banking domain expertise, regulatory knowledge, and advanced data modeling skills creates a highly specialized talent pool, while digital transformation initiatives across UAE's banking sector drive strong hiring demand.

Important Skills

Critical Skills

Banking domain expertise is absolutely essential as it enables the translation of complex financial business requirements into accurate data structures. Without deep understanding of banking operations, risk management, and regulatory requirements, data models may fail to support critical business functions or compliance obligations. Regulatory compliance knowledge, particularly with BCBS 239, Basel III, and regional requirements, is fundamental to success in this role. Financial institutions face severe penalties for compliance failures, making this expertise directly tied to business risk management and organizational success. Advanced data modeling techniques including dimensional modeling and data vault methodologies are critical for designing scalable, maintainable data architectures that can evolve with changing business requirements and technology platforms. SQL and database platform expertise across multiple systems (Oracle, SQL Server, PostgreSQL, DB2) is essential for implementing and validating data models, as well as troubleshooting performance and data quality issues in production environments.

Beneficial Skills

Cloud platform expertise, particularly with Azure, is increasingly valuable as financial institutions migrate to cloud-based architectures for improved scalability, cost efficiency, and disaster recovery capabilities. Big data technologies (Hadoop, Spark) and streaming platforms (Confluent) knowledge is becoming more important as banks seek to process larger volumes of data in real-time for fraud detection, risk management, and customer analytics. Master Data Management (MDM) and metadata management experience is highly beneficial for enterprise-wide data governance initiatives and regulatory reporting requirements that demand consistent, high-quality reference data. Real-time data modeling expertise for payments and fraud detection is increasingly valuable as financial institutions compete on speed and security in digital payment processing and risk mitigation.

Unique Aspects

The role uniquely combines enterprise-scale data modeling with specialized banking domain knowledge, creating opportunities to work on complex regulatory compliance and risk management challenges that are critical to financial institution operations.
Dubai's position as a regional financial hub provides exposure to diverse banking models, from traditional Islamic banking to modern digital banking platforms, offering unique learning and development opportunities.
The focus on both traditional data warehouse architectures and modern cloud-based data lakes reflects the industry's transition period, providing valuable experience with hybrid and migration scenarios.
The emphasis on M&A data integration projects offers specialized experience that is highly valued in the financial services industry and transferable across global markets.

Career Growth

Progression to senior data architect roles typically occurs within 3-5 years, while advancement to management positions or specialized leadership roles may take 5-7 years depending on organizational growth and individual performance.

Potential Next Roles

Enterprise Data Architect positions focusing on overall data strategy and technology roadmap development Data Governance Manager roles responsible for establishing and enforcing data policies across the organization Chief Data Officer or Head of Data positions in smaller financial institutions or fintech companies Solution Architect roles specializing in financial services data and analytics platforms Independent consulting positions providing data modeling expertise to multiple financial institutions

Company Overview

Dicetek LLC

Dicetek LLC appears to be a technology consulting firm specializing in data management and analytics solutions for financial services clients. The company likely provides specialized expertise in enterprise data architecture, regulatory compliance, and digital transformation initiatives for banking and financial institutions.

As a specialized consulting firm, Dicetek operates in the competitive technology services market, focusing on high-value, expertise-driven engagements with financial services clients. The company's positioning suggests a focus on complex, enterprise-level data management challenges rather than commodity IT services.
The Dubai location indicates the company serves the Middle East and North Africa (MENA) financial services market, positioning itself to support the region's banking sector digital transformation initiatives and regulatory compliance requirements.
The consulting environment typically emphasizes professional development, client interaction, and project-based work with opportunities for diverse industry exposure. The specialized nature of financial services data modeling suggests a culture that values deep expertise and continuous learning.
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