{"schemaVersion":"jobsearcher.job.v1","id":"2ceb3e8fd86deee4ac35d4e4","url":"https://jobsearcher.com/jobs/2ceb3e8fd86deee4ac35d4e4","canonicalUrl":"https://jobsearcher.com/jobs/2ceb3e8fd86deee4ac35d4e4","title":"Lead Enterprise Data Architect","description":"Enterprise Data Architect\r\nWe are seeking an experienced and passionate Enterprise Data Architect to build and own foundational enterprise data management capabilities spanning Master Data Management (MDM), Data Governance, Data Quality, Metadata & Cataloging, semantic/context layer engineering, and enterprise data architecture. This role combines strategic leadership with hands-on technical expertise to ensure enterprise data is trusted, governed, discoverable, and ready for analytics, AI, and operational use.\r\nThe Enterprise Data Architect designs, governs, and evolves the enterprise-wide data architecture that powers analytics, AI, and operational workflows. You will define standards and reference architectures; guide data modeling and integration patterns; and influence platform decisions across the enterprise data hub/warehouse ecosystem, MDM, governance, and metadata capabilities.\r\nEnterprise Data Architecture Leadership\r\nDefine and maintain the enterprise data architecture strategy, reference models, and standards\r\nCreate and govern canonical data models, domain models, and integration patterns\r\nEnsure architectural alignment across data engineering, analytics, MDM, governance, and application teams\r\nDrive modernization toward cloud-native, scalable, AI-ready architectures\r\nDefine architecture guardrails for data security, privacy, and regulatory compliance in partnership with Security and Legal (e.g., access controls, classification, retention)\r\nData Modeling & Canonical Design\r\nLead design of conceptual, logical, and physical data models across domains\r\nEstablish enterprise-wide modeling standards, naming conventions, and modeling patterns\r\nPartner with MDM and governance teams to ensure consistency across master data, reference data, and operational data\r\nSemantic / Context Layer Architecture\r\nArchitect and maintain the enterprise context layer (semantic layer) enabling consistent metrics, definitions, and reusable data entities\r\nDefine metric logic, dimensional models, and semantic relationships used across BI, AI, and operational systems\r\nEnsure alignment with analytics engineering (dbt, metric stores, semantic tools)\r\nMaster Data & Governance Architecture\r\nArchitect MDM solutions including domain models, match/merge logic, hierarchies, and integration patterns\r\nPartner with governance teams to operationalize policies through technology\r\nIntegrate metadata, lineage, and governance workflows into the architecture\r\nData Integration & Platform Architecture\r\nDefine ingestion, transformation, and consumption patterns across batch, streaming, and API-based pipelines\r\nArchitect cloud data platforms (Azure/AWS/GCP) including lakehouse, warehouse, and real-time components\r\nMetadata, Catalog, and Lineage Architecture\r\nEnsure scalability, performance, security, and cost optimization\r\nDesign metadata ingestion patterns and lineage frameworks across pipelines, BI tools, and MDM systems\r\nImplement enterprise cataloging solutions using platforms such as Collibra, Atlan, Alation, or similar\r\nEnsure metadata is complete, accurate, and actionable for governance and engineering teams\r\nHands-On Technical Execution\r\nBuild and validate architectural prototypes, POCs, and reference implementations\r\nWrite SQL, design schemas, build lineage connectors, and define transformation logic\r\nTroubleshoot complex data architecture issues across pipelines, models, and platforms\r\nCross-Functional Leadership\r\nPartner with data engineering, analytics, MDM, governance, product, and application teams\r\nProvide architectural guidance, code reviews, and technical mentorship\r\nCommunicate architectural decisions to executives, engineers, and business stakeholders\r\nYOU MUST HAVE\r\n8+ years of experience in data architecture, data engineering, or enterprise architecture\r\nDeep hands-on experience with cloud data platforms (Snowflake, Databricks, Azure, AWS, or GCP)\r\nStrong expertise in data modeling (dimensional, relational, canonical, semantic)\r\nExperience architecting MDM and governance solutions using Collibra, Reltio, Atlan, Informatica, or similar\r\nStrong SQL, data pipeline design, and metadata/lineage engineering skills\r\nExperience with modern data stack tools (dbt, Spark, Kafka, Airflow, etc.)\r\nAbility to translate business needs into scalable architectural designs\r\nExperience with enterprise architecture frameworks (TOGAF, DAMA-DMBOK)\r\nBackground in designing AI-ready data architectures (feature stores, vector stores, semantic layers)\r\nExperience with API-driven architectures and event-driven patterns\r\nFamiliarity with data products and data mesh concepts\r\nAdoption of standardized data models and architectural patterns across the enterprise\r\nReduction in data duplication, inconsistencies, and integration complexity\r\nHigh-quality, governed, discoverable data powering analytics and AI\r\nScalable, cost-efficient cloud data platform performance\r\nStrong alignment between business, engineering, and governance teams\r\nWE VALUE\r\nExperience with enterprise architecture frameworks (TOGAF, DAMA-DMBOK)\r\nBackground in designing AI-ready data architectures (feature stores, vector stores, semantic layers)\r\nExperience with API-driven architectures and event-driven patterns\r\nFamiliarity with data products and data mesh concepts\r\nSuccess Measures\r\nAdoption of standardized data models and architectural patterns across the enterprise\r\nReduction in data duplication, inconsistencies, and integration complexity\r\nHigh-quality, governed, discoverable data powering analytics and AI\r\nScalable, cost-efficient cloud data platform performance\r\nStrong alignment between business, engineering, and governance teams\r\nJ-18808-Ljbffr","company":"Resideo Technologies","rawCompany":"resideo technologies","city":"St Louis","state":"MO","isRemote":false,"isActive":false,"createdAt":"2026-07-16T02:02:11.402Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1243.00","title":"Database Architects","slug":"database-architects"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Lead Enterprise Data Architect","description":"Enterprise Data Architect\r\nWe are seeking an experienced and passionate Enterprise Data Architect to build and own foundational enterprise data management capabilities spanning Master Data Management (MDM), Data Governance, Data Quality, Metadata & Cataloging, semantic/context layer engineering, and enterprise data architecture. This role combines strategic leadership with hands-on technical expertise to ensure enterprise data is trusted, governed, discoverable, and ready for analytics, AI, and operational use.\r\nThe Enterprise Data Architect designs, governs, and evolves the enterprise-wide data architecture that powers analytics, AI, and operational workflows. You will define standards and reference architectures; guide data modeling and integration patterns; and influence platform decisions across the enterprise data hub/warehouse ecosystem, MDM, governance, and metadata capabilities.\r\nEnterprise Data Architecture Leadership\r\nDefine and maintain the enterprise data architecture strategy, reference models, and standards\r\nCreate and govern canonical data models, domain models, and integration patterns\r\nEnsure architectural alignment across data engineering, analytics, MDM, governance, and application teams\r\nDrive modernization toward cloud-native, scalable, AI-ready architectures\r\nDefine architecture guardrails for data security, privacy, and regulatory compliance in partnership with Security and Legal (e.g., access controls, classification, retention)\r\nData Modeling & Canonical Design\r\nLead design of conceptual, logical, and physical data models across domains\r\nEstablish enterprise-wide modeling standards, naming conventions, and modeling patterns\r\nPartner with MDM and governance teams to ensure consistency across master data, reference data, and operational data\r\nSemantic / Context Layer Architecture\r\nArchitect and maintain the enterprise context layer (semantic layer) enabling consistent metrics, definitions, and reusable data entities\r\nDefine metric logic, dimensional models, and semantic relationships used across BI, AI, and operational systems\r\nEnsure alignment with analytics engineering (dbt, metric stores, semantic tools)\r\nMaster Data & Governance Architecture\r\nArchitect MDM solutions including domain models, match/merge logic, hierarchies, and integration patterns\r\nPartner with governance teams to operationalize policies through technology\r\nIntegrate metadata, lineage, and governance workflows into the architecture\r\nData Integration & Platform Architecture\r\nDefine ingestion, transformation, and consumption patterns across batch, streaming, and API-based pipelines\r\nArchitect cloud data platforms (Azure/AWS/GCP) including lakehouse, warehouse, and real-time components\r\nMetadata, Catalog, and Lineage Architecture\r\nEnsure scalability, performance, security, and cost optimization\r\nDesign metadata ingestion patterns and lineage frameworks across pipelines, BI tools, and MDM systems\r\nImplement enterprise cataloging solutions using platforms such as Collibra, Atlan, Alation, or similar\r\nEnsure metadata is complete, accurate, and actionable for governance and engineering teams\r\nHands-On Technical Execution\r\nBuild and validate architectural prototypes, POCs, and reference implementations\r\nWrite SQL, design schemas, build lineage connectors, and define transformation logic\r\nTroubleshoot complex data architecture issues across pipelines, models, and platforms\r\nCross-Functional Leadership\r\nPartner with data engineering, analytics, MDM, governance, product, and application teams\r\nProvide architectural guidance, code reviews, and technical mentorship\r\nCommunicate architectural decisions to executives, engineers, and business stakeholders\r\nYOU MUST HAVE\r\n8+ years of experience in data architecture, data engineering, or enterprise architecture\r\nDeep hands-on experience with cloud data platforms (Snowflake, Databricks, Azure, AWS, or GCP)\r\nStrong expertise in data modeling (dimensional, relational, canonical, semantic)\r\nExperience architecting MDM and governance solutions using Collibra, Reltio, Atlan, Informatica, or similar\r\nStrong SQL, data pipeline design, and metadata/lineage engineering skills\r\nExperience with modern data stack tools (dbt, Spark, Kafka, Airflow, etc.)\r\nAbility to translate business needs into scalable architectural designs\r\nExperience with enterprise architecture frameworks (TOGAF, DAMA-DMBOK)\r\nBackground in designing AI-ready data architectures (feature stores, vector stores, semantic layers)\r\nExperience with API-driven architectures and event-driven patterns\r\nFamiliarity with data products and data mesh concepts\r\nAdoption of standardized data models and architectural patterns across the enterprise\r\nReduction in data duplication, inconsistencies, and integration complexity\r\nHigh-quality, governed, discoverable data powering analytics and AI\r\nScalable, cost-efficient cloud data platform performance\r\nStrong alignment between business, engineering, and governance teams\r\nWE VALUE\r\nExperience with enterprise architecture frameworks (TOGAF, DAMA-DMBOK)\r\nBackground in designing AI-ready data architectures (feature stores, vector stores, semantic layers)\r\nExperience with API-driven architectures and event-driven patterns\r\nFamiliarity with data products and data mesh concepts\r\nSuccess Measures\r\nAdoption of standardized data models and architectural patterns across the enterprise\r\nReduction in data duplication, inconsistencies, and integration complexity\r\nHigh-quality, governed, discoverable data powering analytics and AI\r\nScalable, cost-efficient cloud data platform performance\r\nStrong alignment between business, engineering, and governance teams\r\nJ-18808-Ljbffr","datePosted":"2026-07-16T02:02:11.402Z","dateModified":"2026-07-16T02:02:11.402Z","hiringOrganization":{"@type":"Organization","name":"Resideo Technologies","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"St Louis","addressRegion":"MO","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"2ceb3e8fd86deee4ac35d4e4"},"url":"https://jobsearcher.com/jobs/2ceb3e8fd86deee4ac35d4e4"}}