Contract to Hire
Salary: Open
Bill Rate: Open
Location: Bloomington, MN. Hybrid: (Onsite Tue, Wed, Thur)
Top Skills:
- 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.
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Ideas Revenue Solutions
Sr Data Architect
Location: Bloomington, MN (Weekly Hybrid)
Contract to Hire
Top Skills:
- 7 Years of Data Architecture Experience
- AWS, Data warehousing, Data Lakes, ect.
Position Summary
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
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.
Key Responsibilities
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.
Required Qualifications
- 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.
