Sr Data Architect

Ideas Revenue Solutions Sr Data Architect

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.

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

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

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

Technology stack is less important than demonstrated architecture judgment, governance leadership, and enterprise semantic modeling experience.

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.

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.

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.

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.

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.

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.

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.