Sr Data Architect

• 7 Years of Data Architecture Experience
• AWS, Data warehousing, Data Lakes, ect., We are seeking a highly experienced Senior Data Architect to lead the design, governance, and evolution of our enterprise data architecture. This role will establish and maintain scalable, business-aligned data ecosystems that enable analytics, reporting, AI-driven insights, and autonomous decision-making, Enterprise Data Architecture
• Define and maintain the enterprise data architecture strategy, roadmap, standards, and reference architectures.
• Design scalable data ecosystems spanning operational systems, data warehouses, data lakes, analytical platforms, and AI-enabled data products.
• Establish architecture patterns that promote consistency, data quality, reusability, interoperability, and technical sustainability across domains.
• Drive architectural decisions that balance business needs, governance requirements, scalability, and long-term maintainability.

Data Warehousing & Data Lakes

• Design and oversee enterprise data warehouse and data lake architectures.
• Develop strategies for data ingestion, integration, transformation, storage, and consumption.
• Define best practices for historical data management, dimensional modeling, analytical data structures, and governed data consumption.

Data Modeling & Semantic Layers

• Lead the development and governance of conceptual, logical, and physical data models.
• Define enterprise-wide business vocabularies, canonical data models, and domain-aligned information structures.
• Design, implement, and maintain semantic layers that provide consistent business definitions, metrics, entities, and relationships across reporting, analytics, AI, and operational systems.
• Partner with business stakeholders to align semantic models with business processes, KPIs, and decision-making workflows.
• Evangelize semantic-first architecture approaches that improve data discoverability, trust, self-service analytics, AI grounding, and operational consistency.

Data Governance & Data Management

• Establish and drive enterprise data governance frameworks, policies, standards, and stewardship practices.
• Define ownership models, metadata management strategies, and data lifecycle controls.
• Implement governance practices for data quality, lineage, cataloging, classification, privacy, compliance, and retention.

AI, Knowledge Graphs & Autonomous Workflows

• Collaborate with AI, analytics, and platform teams to ensure data architectures support AI-ready enterprise capabilities.
• Define semantic foundations that enable knowledge graphs, enterprise context models, intelligent data discovery, and governed AI consumption.
• Evaluate and design architectures supporting AI agents, autonomous workflows, retrieval-augmented systems, and machine-assisted decision-making.

Leadership & Collaboration

• Serve as a senior advisor to architecture, engineering, analytics, product, and business leadership teams.
• Mentor architects, engineers, analysts, and governance stakeholders on data architecture and semantic modeling best practices.
• Facilitate cross-functional alignment on enterprise data standards, governance initiatives, and semantic layer adoption.

• 7 Years of Data Architecture Experience
• AWS, Data warehousing, Data Lakes, ect.
• Local Only, Twin Cities. Hybrid

Experience Level: 7+ years of demonstrated experience building enterprise data architecture and governance initiatives.

Core Domains: AWS. Data warehousing, data lakes, data modeling, data governance, metadata, lineage, and semantic layers.

Strategic Emphasis: Business-aligned semantic models, governed data products, AI-ready data foundations, and reusable enterprise data capabilities.

Technology Posture: Technology stack is less important than demonstrated architecture judgment, governance leadership, and enterprise semantic modeling experience. -, capabilities across the organization. The ideal candidate combines deep expertise in data warehousing, data lakes, data modeling, data governance, and semantic layer design with a strategic mindset for building enterprise-wide data programs. Technology stack experience is helpful, but this role emphasizes architecture principles, governance practices, and semantic modeling expertise over specific platforms or vendors.

Experience Level: 7+ years of demonstrated experience building enterprise data architecture and governance initiatives.

Core Domains: Data warehousing, data lakes, data modeling, data governance, metadata, lineage, and semantic layers.

Strategic Emphasis: Business-aligned semantic models, governed data products, AI-ready data foundations, and reusable enterprise data capabilities.

Technology Posture: Technology stack is less important than demonstrated architecture judgment, governance leadership, and enterprise semantic modeling experience., * Bachelor’s degree in Computer Science, Information Systems, Data Management, Engineering, or a related

field; equivalent practical experience may be considered.

• 7+ years of demonstrated experience leading enterprise data architecture and governance initiatives.
• Extensive experience designing and implementing enterprise data warehouses, data lakes, analytical platforms, enterprise data models, semantic layers, and data governance programs.