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Dynata
United States · remote · USD 120000-150000 annual
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.
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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, governed, and reusable platform foundations.
Cross-Functional Collaboration
Partner closely with Product, Technology, Research & Data Science, and Data Platform teams.
Translate complex technical concepts into clear architectural decisions and implementation guidance.
Lead architecture discussions that balance business needs, governance requirements, technical feasibility, and long-term scalability.
Serve as a technical thought leader on semantic architecture, data governance, AI enablement, and enterprise platform design.
Qualifications
7+ years of experience in data architecture, platform architecture, AI/ML infrastructure, data engineering, or related fields.
Proven experience designing enterprise-scale semantic layers, data models, schema governance frameworks, or data contract architectures.
Strong understanding of modern lakehouse architectures, medallion design patterns, metadata management, and data governance principles.
Demonstrated experience architecting machine learning and AI platforms, including feature stores, model lifecycle management, lineage, and governance capabilities.
Experience establishing technical standards that support analytics, machine learning, and AI-driven applications at scale.
Strong understanding of schema management, metadata frameworks, versioning strategies, and interoperability patterns.
Experience translating ambiguous business requirements and governance concepts into scalable technicalarchitectures.
Strong communication and stakeholder management skills, including experience influencing technical leaders, architects, and executive stakeholders.
Demonstrated success operating effectively in ambiguous environments and leading foundational platform and architecture initiatives.
Experience with modern data and AI platforms such as Databricks, DataHub, Snowflake, feature stores, metadata platforms, or comparable technologies.
Preferred Qualifications
Experience implementing or governing enterprise semantic layers and business glossaries.
Familiarity with AI governance, model governance, and responsible AI frameworks.
Experience designing architecture for graph analytics, forecasting, optimization, or decision-support systems.
Exposure to LLM-enabled platform capabilities such as metadata generation, semantic modeling, catalog enrichment, or governance automation.
At Dynata, we deliver the highest quality first-party data to help businesses around the world gain precise insights, activate the right audiences, and confidently measure impact. With industry-leading respondent accuracy, reliability, and a commitment to continuous improvement, Dynata is the trusted foundation for smarter decision-making.
At Dynata, we are committed to creating an inclusive and accessible environment where every employee and customer feels valued, respected, and supported. We strive to build a workforce that reflects the diversity of the communities we serve. Dynata welcomes and encourages applications from individuals with disabilities and is dedicated to fostering a work culture that supports everyone. Accommodations are available upon request for all aspects of the selection process.
Dynata is an Equal Opportunity Employer. We consider all qualified applicants and employees without regard to race, color, religion, sex (including pregnancy, sexual orientation, and gender identity), national origin, marital status, age, disability, genetic information, veteran status, or any other legally protected status under applicable laws.
The base salary range for this position in is $120K-$150K/yr; however, base pay offered may vary depending on location, job-related knowledge, skills, and experience. A discretionary incentive program may be provided as part of the compensation package, in addition to a full range of medical and other benefits, dependent on full-time employment status.
Originally posted on Himalayas