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Microsoft Architect Manager Taiwan, Taoyuan City 131616653
02.09.2025
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As anArchitect Managerwithin theGlobal Delivery Center, you will lead a team of high-impact Solution Architects focused on delivering transformational outcomes acrossAI Transformation. You will set the technical vision, guide complex presales and delivery engagements, and influence senior stakeholders to shape strategy and mitigate execution risks. Your leadership will drive innovation, readiness, and scalable delivery through reusable IP, skilling plans, and architectural excellence.
Required/Minimum Qualifications
- 20+ years of overall experience, including 10+ years in leadership roles across architecture and consulting. Proven success leading complex, multi-workload programs in data, analytics, and AI.
- Demonstrated people leadership—hiring, coaching, and performance management—with measurable outcomes in team readiness, innovation, and delivery quality.
- expertisein Azure Data & AI services (e.g., Fabric, Synapse, Databricks, Azure SQL/MI, ADLS, Power BI, Azure OpenAI, vector stores, RAG/agents) and modern integration patterns.
- Strong governance capabilities across well-architected frameworks, including decision logs, NFRs, RAID management, test strategies, and security controls.
- Experience in developing and scaling reusable IP, reference architecture, and automation frameworks.
Additional or Preferred Qualifications
- Certifications: Azure Solutions Architect Expert (AZ-305), Azure AI Engineer, DP-203, DevOps Expert, TOGAFor equivalent technical/architecture certification.
- Experience in developing and scaling reusable IP, referencearchitecture, and automation frameworks.
- track recordin landingData &AI transformation initiatives
- Drive outcomes across strategicareas—Migrate & Modernize Data Estates,Innovate with AI Apps & Agents, andUnify the Data Platform—ensuring measurable value realization.
- Executive Engagement:Influence CXO-level stakeholders, define success metrics, lead Architecture Review Boards, and proactively manage risks, assumptions, and trade-offs.
- Technical Governance at Scale:Enforce architectural rigor through decision logs, traceability, non-functional requirements (NFRs), and secure-by-design principles for resilient data and AI platforms.
- Presales Excellence:
- Readiness & Innovation:Cultivate communities of practice, drive certification plans, and promote reuse of accelerators and IP. Embed Responsible AI standards across engagements.
- Program Oversight:Steer large-scale, multi-workload programs—including data estate modernization, analytics on Fabric, andGenAI
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