Lead Enterprise Data Architect

Overview

Enterprise Data Architect — We are seeking an experienced and passionate Enterprise Data Architect to build and own foundational enterprise data management capabilities spanning Master Data Management (MDM), Data Governance, Data Quality, Metadata & Cataloging, semantic/context layer engineering, and an enterprise data architecture. This role combines strategic leadership with hands-on technical expertise to ensure enterprise data is trusted, governed, discoverable, and ready for analytics, AI, and operational use.

The Enterprise Data Architect designs, governs, and evolves the enterprise-wide data architecture that powers analytics, AI, and operational workflows. You will define standards and reference architectures; guide data modeling and integration patterns; and influence platform decisions across the enterprise data hub/warehouse ecosystem, MDM, governance, and metadata capabilities.

• Define and maintain the enterprise data architecture strategy, reference models, and standards
• Create and govern canonical data models, domain models, and integration patterns
• Ensure architectural alignment across data engineering, analytics, MDM, governance, and application teams
• Drive modernization toward cloud-native, scalable, AI-ready architectures
• Define architecture guardrails for data security, privacy, and regulatory compliance in partnership with Security and Legal (e.g., access controls, classification, retention)

• Lead design of conceptual, logical, and physical data models across domains
• Establish enterprise-wide modeling standards, naming conventions, and modeling patterns
• Partner with MDM and governance teams to ensure consistency across master data, reference data, and operational data

• Architect and maintain the enterprise context layer (semantic layer) enabling consistent metrics, definitions, and reusable data entities
• Define metric logic, dimensional models, and semantic relationships used across BI, AI, and operational systems
• Ensure alignment with analytics engineering (dbt, metric stores, semantic tools)

• Architect MDM solutions including domain models, match/merge logic, hierarchies, and integration patterns
• Partner with governance teams to operationalize policies through technology
• Integrate metadata, lineage, and governance workflows into the architecture

• Define ingestion, transformation, and consumption patterns across batch, streaming, and API-based pipelines
• Architect cloud data platforms (Azure/AWS/GCP) including lakehouse, warehouse, and real-time components

• Ensure scalability, performance, security, and cost optimization
• Design metadata ingestion patterns and lineage frameworks across pipelines, BI tools, and MDM systems
• Implement enterprise cataloging solutions using platforms such as Collibra, Atlan, Alation, or similar
• Ensure metadata is complete, accurate, and actionable for governance and engineering teams

• Build and validate architectural prototypes, POCs, and reference implementations
• Write SQL, design schemas, build lineage connectors, and define transformation logic
• Troubleshoot complex data architecture issues across pipelines, models, and platforms

• Partner with data engineering, analytics, MDM, governance, product, and application teams
• Provide architectural guidance, code reviews, and technical mentorship
• Communicate architectural decisions to executives, engineers, and business stakeholders

• 8+ years of experience in data architecture, data engineering, or enterprise architecture
• Deep hands-on experience with cloud data platforms (Snowflake, Databricks, Azure, AWS, or GCP)
• Strong expertise in data modeling (dimensional, relational, canonical, semantic)
• Experience architecting MDM and governance solutions using Collibra, Reltio, Atlan, Informatica, or similar
• Strong SQL, data pipeline design, and metadata/lineage engineering skills
• Experience with modern data stack tools (dbt, Spark, Kafka, Airflow, etc.)
• Ability to translate business needs into scalable architectural designs
• Experience with enterprise architecture frameworks (TOGAF, DAMA-DMBOK)
• Background in designing AI-ready data architectures (feature stores, vector stores, semantic layers)
• Experience with API-driven architectures and event-driven patterns
• Familiarity with data products and data mesh concepts
• Adoption of standardized data models and architectural patterns across the enterprise
• Reduction in data duplication, inconsistencies, and integration complexity
• High-quality, governed, discoverable data powering analytics and AI
• Scalable, cost-efficient cloud data platform performance

• Experience with enterprise architecture frameworks (TOGAF, DAMA-DMBOK)
• Background in designing AI-ready data architectures (feature stores, vector stores, semantic layers)
• Experience with API-driven…