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.

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.

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.

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.

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.

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