Data Architect

SFE; Irving (TX); On-site; USD 150,000 - 190,000; Full time

Job summary

SFE is seeking a Data Architect to lead enterprise-wide Data Mesh and Data Lakehouse initiatives, driving architecture, governance, and product-driven data delivery across business domains.

The candidate will define standards for metadata, lineage, quality, observability, and discoverability, and will modernize legacy systems to cloud-native platforms supporting BI and AI/ML use cases.

Qualifications

• 12+ years in Data Architecture, Data Engineering, or Enterprise Architecture.
• Experience implementing Data Mesh Architecture and Data as a Product operating models.
• Experience with Enterprise Data Lakes/Lakehouses and Federated Governance.
• Hands-on experience with self-service data platforms and cloud data platforms.

Responsibilities

• 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 and support BI, AI/ML, GenAI use cases.
• Lead modernization from legacy warehouses to modern cloud ecosystems.
• Implement federated governance, privacy, security, and compliance controls by design.

Skills

Data Mesh Architecture

Data as a Product

Enterprise Data Lakes/Lakehouses

Federated Data Governance

Self-Service Data Platforms

Tools

Databricks

Snowflake

Microsoft Fabric

Azure Data Platform

AWS Data Services

Google Cloud Data Platform

Job description

Data Architect

Location- Irving TX OR Tampa, FL

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