Data Mesh Data Architect

Data Mesh & Data Product Architecture

• Define and drive enterprise-wide Data Mesh strategy, architecture, and operating model.
• Establish domain-oriented data ownership and federated governance frameworks.
• Lead the design, lifecycle management, and adoption of Data Products.
• Define standards for data contracts, interoperability, metadata, lineage, quality, observability, and discoverability.
• Enable self-service data consumption and product-driven data delivery across business domains.

Data Lake / Lakehouse Architecture

• Design scalable cloud-native Data Lake/Lakehouse platforms supporting structured, semi-structured, and unstructured data.
• Define architectures for batch, streaming, API, and event-driven integration patterns.
• Create target-state architectures supporting BI, Advanced Analytics, AI/ML, and GenAI use cases.
• Lead modernization initiatives from legacy data warehouses and siloed platforms to modern cloud ecosystems.

Governance, Security & Quality

• Implement federated governance, privacy, security, and compliance controls by design.
• Establish enterprise standards for data quality, observability, lineage, cataloging, and monitoring.
• Ensure compliance with regulatory and enterprise security requirements.

Leadership & Stakeholder Management

• Develop reference architectures, roadmaps, standards, and reusable design patterns.
• Provide architecture leadership across business, technology, data engineering, governance, and cloud teams.
• Engage with CDOs, CIOs, business leaders, and domain owners to align architecture with business outcomes.
• Mentor architects and engineering teams on modern data architecture practices.

Required Experience

• 12+ years in Data Architecture, Data Engineering, or Enterprise Architecture.
• Proven experience implementing:
• Data Mesh Architecture
• Data as a Product Operating Models
• Enterprise Data Lakes/Lakehouses
• Federated Data Governance
• Self-Service Data Platforms
• Hands-on experience with cloud data platforms such as:
• Databricks
• Snowflake
• Microsoft Fabric
• Azure Data Platform
• AWS Data Services
• Google Cloud Data Platform

Strong knowledge of metadata management, lineage, data quality, observability, DataOps, and CI/CD