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Data Solution Architect

Auxo Talent
Dubai, UAE
Full-time
Mid-Senior
Onsite
Discovered 1 weeks ago
Data architectureData strategySolution architectureEnterprise data architectureData governanceData quality and metadata management
Free

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Role Overview

Design and deliver secure, scalable, and citizen-centric data solutions aligned with Enterprise Architecture and digital transformation strategies.

Lead data architecture, governance, cloud platforms, analytics, AI/ML, and Agentic AI capabilities in a regulated or government environment.

Core Responsibilities

  • Develop enterprise data architecture and strategy for digital transformation and modernization.
  • Design scalable data platforms, lakehouses, data lakes, and end-to-end batch and real-time data pipelines.
  • Define and implement Data and AI governance frameworks, operating models, quality standards, metadata, lineage, classification, and retention.
  • Architect AI/ML, GenAI, LLMOps, AI Engineering, and Agentic AI solutions for enterprise use cases.

Architecture and Modelling

  • Design Data Mesh, Data Fabric, Ontology, and Semantic Model architecture patterns.
  • Define conceptual, logical, and physical data models, including Star, Snowflake, and Data Vault models.
  • Ensure data solutions satisfy security, privacy, regulatory, and national data protection requirements.

Stakeholder and Platform Delivery

  • Lead architecture workshops and translate business and government requirements into technical solutions and roadmaps.
  • Support technology evaluation, procurement, vendor selection, and implementation of data and AI platforms.

Experience and Skills

  • Require experience in data architecture, data strategy, and solution architecture.
  • Experience in government or highly regulated environments is preferred.
  • Require strong hands-on experience with one or more of Azure, AWS, GCP, Snowflake, Databricks, Delta Lake, or Apache Iceberg.
  • Require experience architecting batch and event-driven or real-time data integration solutions.

Governance and AI Requirements

  • Require strong knowledge of data governance, data quality, security, compliance, metadata, and lineage.
  • Require experience designing AI/ML and GenAI architectures, including LLMs, Agentic AI, LangChain or LangGraph, MCP, prompt or context engineering, and AI evaluation.
  • Require strong understanding of Enterprise Architecture principles and digital transformation strategies.

Professional Capabilities

  • Require stakeholder management, communication, workshop facilitation, and technical leadership skills.

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