Platform Architect - Data Platform

Job Description RAKBANK is looking for a highly experienced Data Platform Architect to lead the architecture, strategy, governance, and evolution of the Bank’s Enterprise Data Platform. This role is responsible for defining and governing scalable, secure, cloud-native data platforms that support analytics, AI/ML, regulatory reporting, customer intelligence, and enterprise-wide data products. You will play a critical role in shaping RAKBANK’s modern data ecosystem, driving architecture decisions across Databricks Lakehouse, Azure, Kafka, Informatica, Power BI, Data Governance, and AI-ready data foundations. What You Will Be Doing Enterprise Data Platform Strategy & Architecture • Define and maintain the Enterprise Data Platform target-state architecture and roadmap. • Establish architecture standards, principles, reusable patterns, and reference architectures. • Ensure alignment with enterprise architecture, security, compliance, and cloud strategy. • Lead architecture decisions supporting analytics, AI/ML, reporting, and regulatory capabilities. Platform Ownership & Governance • Own platform architecture standards across Databricks, Delta Lake, Azure Data Factory, Confluent Kafka, Informatica/IICS, metadata, and data quality services. • Drive platform scalability, resilience, observability, performance, and cost optimization. • Define engineering standards, automation frameworks, CI/CD practices, and operating models. Data Governance, Privacy & Compliance • Embed governance, lineage, metadata, quality, retention, access control, encryption, masking, and privacy-by-design principles. • Ensure compliance with UAE PDPL, CBUAE regulations, internal policies, and audit requirements. • Partner closely with Data Governance, Information Security, Risk, and Compliance teams. Integration & Streaming Architecture • Define enterprise integration standards for batch, real-time, and event-driven architectures using Kafka, CDC, APIs, and ETL/ELT. • Govern integration patterns across on-premises, cloud, SaaS, and partner ecosystems. • Eliminate non-standard point-to-point integrations and uncontrolled data movement. AI & Advanced Analytics Enablement • Design trusted and governed foundations for AI, Machine Learning, GenAI, and advanced analytics. • Enable reusable data products, domain data models, and AI-ready datasets. • Ensure responsible AI principles, including explainability, lineage, privacy, and observability. Architecture Leadership • Review and approve platform solution designs and technology selections. • Present architecture proposals and exceptions to Enterprise Design Authority (EDA). • Mentor architects and engineering teams while driving strategic technology decisions. What We Are Looking For • 12+ years of experience across Data Architecture, Data Platform Architecture, Solution Architecture, or Enterprise Architecture. • Minimum 5 years leading enterprise-scale data platform transformation initiatives. • Strong experience within Banking or Financial Services environments. • Proven delivery of Databricks-based Lakehouse architectures and AI-enabled data platforms. • Experience establishing enterprise data governance frameworks, standards, and architecture guardrails. • Bachelor’s degree in computer science, Information Systems, Engineering, Data Science, or a related field. • Master’s Degree is preferred. • Professional Certifications (Preferred) • Databricks Certified Data Engineer Professional • Databricks Solution Architect • Microsoft Azure Solutions Architect Expert • Azure Data Engineer Associate • TOGAF • CDMP Technical Expertise • Databricks Lakehouse, Delta Lake, Spark, PySpark, Python, SQL. • Azure Data Factory, Confluent Kafka, CDC, Event-Driven Architecture. • Data Mesh, Data Products, Metadata Management, Data Lineage, Data Quality. • Azure, AWS, Hybrid Cloud Architecture, Infrastructure-as-Code, DevOps, CI/CD. • Power BI, Semantic Models, Self-Service Analytics. • Privacy Controls, Encryption, Tokenization, RBAC/ABAC, Zero Trust Security. Show more Show less

12+ years of experience across Data Architecture, Data Platform Architecture, Solution Architecture, or Enterprise Architecture. Minimum 5 years leading enterprise-scale data platform transformation initiatives. Strong experience within Banking or Financial Services environments. Proven delivery of Databricks-based Lakehouse architectures and AI-enabled data platforms. Experience establishing enterprise data governance frameworks, standards, and architecture guardrails. Bachelor’s degree in computer science, Information Systems, Engineering, Data Science, or a related field. Master’s Degree is preferred. Professional Certifications (Preferred) such as Databricks Certified Data Engineer Professional, Databricks Solution Architect, Microsoft Azure Solutions Architect Expert, Azure Data Engineer Associate, TOGAF, CDMP.

Define and maintain the Enterprise Data Platform target-state architecture and roadmap. Establish architecture standards, principles, reusable patterns, and reference architectures. Ensure alignment with enterprise architecture, security, compliance, and cloud strategy. Lead architecture decisions supporting analytics, AI/ML, reporting, and regulatory capabilities. Own platform architecture standards across Databricks, Delta Lake, Azure Data Factory, Confluent Kafka, Informatica/IICS, metadata, and data quality services. Drive platform scalability, resilience, observability, performance, and cost optimization. Define engineering standards, automation frameworks, CI/CD practices, and operating models. Embed governance, lineage, metadata, quality, retention, access control, encryption, masking, and privacy-by-design principles. Ensure compliance with UAE PDPL, CBUAE regulations, internal policies, and audit requirements. Partner closely with Data Governance, Information Security, Risk, and Compliance teams. Define enterprise integration standards for batch, real-time, and event-driven architectures using Kafka, CDC, APIs, and ETL/ELT. Govern integration patterns across on-premises, cloud, SaaS, and partner ecosystems. Eliminate non-standard point-to-point integrations and uncontrolled data movement. Design trusted and governed foundations for AI, Machine Learning, GenAI, and advanced analytics. Enable reusable data products, domain data models, and AI-ready datasets. Ensure responsible AI principles, including explainability, lineage, privacy, and observability. Review and approve platform solution designs and technology selections. Present architecture proposals and exceptions to Enterprise Design Authority (EDA). Mentor architects and engineering teams while driving strategic technology decisions.