Azure Data Architect

Strong Azure data ecosystem experience, including Databricks, ADLS Gen2, Synapse, Microsoft Fabric, Azure Data Factory, Spark, and distributed platforms. Lead architecture for Azure-native data platforms, including Databricks, data lake, orchestration, warehouse, BI, and distributed processing ecosystems.

This is a hands-on architecture position for someone who can design scalable cloud data platforms, establish reusable standards, and partner across engineering, governance, cybersecurity, risk, compliance, and business teams. The right candidate will bring both strategic architecture judgment and practical implementation depth.

RESPONSIBILITIES

• Define enterprise data architecture strategy, roadmaps, standards, and target-state designs.
• Design secure, scalable cloud data architectures across lakehouse, analytics, governance, and AI/ML use cases.
• Lead architecture for Azure-native data platforms, including Databricks, data lake, orchestration, warehouse, BI, and distributed processing ecosystems.
• Establish standards for data modeling, metadata, lineage, semantic layers, reference architecture, integration patterns, and lifecycle management.
• Drive AI-ready architecture initiatives, including semantic modeling, vector-ready patterns, metadata-driven frameworks, and scalable data pipelines.
• Partner with governance, risk, compliance, cybersecurity, and legal teams to support privacy, retention, auditability, and regulatory controls.
• Review enterprise architecture designs to ensure alignment with approved standards and security principles.
• Advise senior leaders by translating complex technical topics into practical business and technology recommendations.

QUALIFICATIONS

• Enterprise data architecture, solution architecture, data engineering, or platform modernization experience.
• Hands-on experience designing production-grade enterprise data architectures.
• Strong Azure data ecosystem experience, including Databricks, ADLS Gen2, Synapse, Microsoft Fabric, Azure Data Factory, Spark, and distributed platforms.
• Deep knowledge of data modeling, metadata, lineage, governance, lifecycle management, master/reference data, and integration architecture.
• Understanding of AI/ML architecture needs, including AI-ready pipelines, semantic modeling, feature engineering support, and analytical data environments.
• Experience with architecture and governance tools such as Erwin, ER/Studio, Collibra, Microsoft Purview, Alation, Visio, Lucidchart, or equivalents.
• Familiarity with lakehouse, APIs, event-driven architecture, data mesh, data fabric, and distributed processing patterns.
• Experience supporting data quality, security, privacy, access governance, auditability, and regulatory compliance.
• Strong communication skills with the ability to influence technical and non-technical stakeholders.
• Mandarin speaking ability.