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Rakbank Linkedin · Posted 14d ago

Platform Architect - Data Platform

United Arab Emirates

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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.
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