Data Architect – Advanced Analytics & Data Intelligence

Urgent requirement for Data Architect – Advanced Analytics & Data Intelligence with experience in Redshift Spectrum in Retail, Corporate or SME domain is required for our banking clients in Abu Dhabi & Dubai,UAE 10+ years in Data Architecture/Data Warehousing with strong Banking domain experience.--MustExpertise in Data Modelling (Kimball, Star Schema, Snowflake, Data Vault) and Enterprise Data Architecture.--MustHands-on AWS experience including Redshift, Glue, S3, Lambda, Airflow, and Cloud-based Analytics Platforms.--MustExperience designing data platforms supporting Analytics, AI/ML, Data Governance, and Regulatory Reporting.--Must Role Overview We are seeking an experienced Data Modeller with strong banking domain expertise to design and implement enterprise-scale analytical data models that support Advanced Analytics, AI, Machine Learning, MIS, and Business Intelligence initiatives. The ideal candidate will be responsible for designing conceptual, logical, and physical data models, developing dimensional data marts, and ensuring data consistency, quality, and governance across the enterprise.Data Modelling & Architecture • Design conceptual, logical, and physical data models for Retail, Corporate, and SME Banking analytics use cases. • Develop dimensional models using Kimball methodology including Fact Tables, Conformed Dimensions, Aggregate Tables, and Bridge Tables. • Implement Slowly Changing Dimensions (SCD Type 1, 2, and 3). • Design and maintain Star Schema, Snowflake Schema, and Data Vault models. • Create and maintain enterprise data dictionaries, metadata, and source-to-target mappings. • Establish data modelling standards, governance frameworks, and naming conventions. Data Warehouse & Data Mart Design • Design and maintain analytical data marts supporting reporting, AI, ML, and business analytics. • Build customer, account, product, channel, branch, collateral, and relationship dimension models. • Develop historical and point-in-time structures to support advanced analytics and predictive modelling. • Ensure scalability, performance, and reusability of enterprise data models. Data Governance & Quality • Maintain data lineage, metadata repositories, and business glossary definitions. • Define and implement data quality rules, validation frameworks, and reconciliation controls. • Ensure compliance with banking data governance, privacy, and security policies. • Collaborate with data owners to improve data accuracy, consistency, and integrity. Business & Analytics Enablement • Partner with Data Engineers, Data Scientists, AI/ML teams, and business stakeholders to translate business requirements into scalable data models. • Design analytical datasets supporting customer segmentation, profitability, risk, portfolio analysis, churn, fraud, and predictive analytics. • Support Business Intelligence and self-service reporting initiatives through governed semantic models. Documentation & Best Practices • Produce technical design documents, entity relationship diagrams (ERD), data flow diagrams, and modelling standards. • Conduct model reviews and ensure adherence to enterprise architecture standards. • Mentor team members on dimensional modelling best practices and database design principles. Required Experience • 7+ years of experience in Data Modelling, Data Warehousing, or Data Architecture. • Minimum 4+ years of experience within Banking or Financial Services. • Proven experience designing enterprise-scale dimensional data models and analytical data marts. • Strong experience supporting Data Analytics, Business Intelligence, and AI/ML platforms. • Experience working on cloud-based data platforms, preferably AWS. Required Technical SkillsData Modelling • Kimball Dimensional Modelling • Star Schema • Snowflake Schema • Data Vault Modelling • Fact & Dimension Modelling • SCD Type 1, 2 & 3 • Data Mart Architecture • Enterprise Data Warehouse Design • Metadata Management • Data Lineage & Governance Database Technologies • MS SQL Server • T-SQL • SQL Performance Tuning • Stored Procedures • Amazon Redshift • Database Design & Optimization Cloud Technologies • AWS S3 • Amazon Redshift • AWS Glue • AWS Lambda • CloudWatch • IAM Fundamentals Data Governance • Data Quality Frameworks • Metadata Management • Business Glossary • Data Classification • Data Privacy & Compliance Banking Domain ExpertiseStrong Experience In • Retail Banking (CASA, Deposits, Cards, Loans, Mortgages) • Corporate Banking (Trade Finance, Lending, Cash Management) • SME Banking (Business Accounts, Working Capital, Merchant Data) • Core Banking Data Structures • Customer & Account Hierarchies • Risk Metrics (PD, LGD, EAD, IFRS9) • Regulatory Reporting and MIS Reporting Education • Bachelor's Degree in Computer Science, Information Systems, Engineering, Statistics, Mathematics, or related field. • Master's Degree in Data Science, Analytics, or related discipline preferred Skills: data,analytics,architecture

7+ years of experience in Data Modelling, Data Warehousing, or Data Architecture. Minimum 4+ years of experience within Banking or Financial Services. Proven experience designing enterprise-scale dimensional data models and analytical data marts. Strong experience supporting Data Analytics, Business Intelligence, and AI/ML platforms. Experience working on cloud-based data platforms, preferably AWS.

Design conceptual, logical, and physical data models for Retail, Corporate, and SME Banking analytics use cases. Develop dimensional models using Kimball methodology including Fact Tables, Conformed Dimensions, Aggregate Tables, and Bridge Tables. Implement Slowly Changing Dimensions (SCD Type 1, 2, and 3). Design and maintain Star Schema, Snowflake Schema, and Data Vault models. Create and maintain enterprise data dictionaries, metadata, and source-to-target mappings. Establish data modelling standards, governance frameworks, and naming conventions. Design and maintain analytical data marts supporting reporting, AI, ML, and business analytics. Build customer, account, product, channel, branch, collateral, and relationship dimension models. Develop historical and point-in-time structures to support advanced analytics and predictive modelling. Ensure scalability, performance, and reusability of enterprise data models. Maintain data lineage, metadata repositories, and business glossary definitions. Define and implement data quality rules, validation frameworks, and reconciliation controls. Ensure compliance with banking data governance, privacy, and security policies. Collaborate with data owners to improve data accuracy, consistency, and integrity. Partner with Data Engineers, Data Scientists, AI/ML teams, and business stakeholders to translate business requirements into scalable data models. Design analytical datasets supporting customer segmentation, profitability, risk, portfolio analysis, churn, fraud, and predictive analytics. Support Business Intelligence and self-service reporting initiatives through governed semantic models. Produce technical design documents, entity relationship diagrams (ERD), data flow diagrams, and modelling standards. Conduct model reviews and ensure adherence to enterprise architecture standards. Mentor team members on dimensional modelling best practices and database design principles.