Senior AI Architect, Semantic Layer & Algorithm Ar

Dynata is seeking a Senior AI Architect for Semantic Layer & Algorithm Architecture to lead the design of the foundationalarchitecturesthatpowerthe company's next-generation data, analytics, and AI ecosystem. This Senior AI Architect will define the technical patterns, governance frameworks, and integration standards that connect Dynata 's lakehouse, semantic layer, feature ecosystem, and machine learning capabilities into a scalable and reusable platform. Working at the intersection of data architecture, governance, and AI enablement, the Senior AI Architect will ensure that data assets are discoverable, interoperable, and optimized for analytics, machine learning, and emerging AI applications. Reporting to the VP, Research & Data Science, this role will partner closely with product, engineering, and platform teams to establish the architectural standards that support Dynata 's evolving portfolio of data products, AI capabilities, and enterprise decision-support systems. Key Responsibilities Semantic Layer & Data Architecture Define and evolve the technical architecture for Dynata 's semantic layer, medallion architecture, and enterprise data models. Translate business concepts, governance standards, and domain definitions into scalable technical frameworks and enforceable architectures. Establish standards for schema design, metadata management, interoperability, and semantic consistency across the platform. Ensure analytical, operational, and AI use cases are supported by a common architectural foundation. Data Contracts & Governance Standards Design and govern data contract frameworks that enable reliable, reusable, and trusted data assets across the organization. Establish standards for schema validation, versioning, lineage, quality controls, and controlled evolution of enterprise datasets. Partner with governance stakeholders to operationalize policies through technical controls and platform capabilities. Promote consistency, traceability, and discoverability across enterprise data assets. AI & Algorithm Platform Architecture Define architectural patterns for feature stores, model inputs and outputs, model lifecycle management, and algorithm interoperability. Establish standards for how analytical models, machine learning solutions, and AI services integrate with enterprise data assets. Design scalable frameworks for feature reuse, model governance, and algorithm deployment. Ensure AI and machine learning capabilities are built upon secure,...

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