Lead Enterprise Data Architect

Enterprise Data ArchitectWe 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.The 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.Job DutiesEnterprise Data Architecture LeadershipDefine and maintain the enterprise data architecture strategy, reference models, and standardsCreate and govern canonical data models, domain models, and integration patternsEnsure architectural alignment across data engineering, analytics, MDM, governance, and application teamsDrive modernization toward cloud-native, scalable, AI-ready architecturesDefine architecture guardrails for data security, privacy, and regulatory compliance in partnership with Security and Legal (e.g., access controls, classification, retention)Data Modeling & Canonical DesignLead design of conceptual, logical, and physical data models across domainsEstablish enterprise-wide modeling standards, naming conventions, and modeling patternsPartner with MDM and governance teams to ensure consistency across master data, reference data, and operational dataSemantic / Context Layer ArchitectureArchitect and maintain the enterprise context layer (semantic layer) enabling consistent metrics, definitions, and reusable data entitiesDefine metric logic, dimensional models, and semantic relationships used across BI, AI, and operational systemsEnsure alignment with analytics engineering (dbt, metric stores, semantic tools)Master Data & Governance ArchitectureArchitect MDM solutions including domain models, match/merge logic, hierarchies, and integration patternsPartner with governance teams to operationalize policies through technologyIntegrate metadata, lineage, and governance workflows into the architectureData Integration & Platform ArchitectureDefine ingestion, transformation, and consumption patterns across batch, streaming, and API-based pipelinesArchitect cloud data platforms (Azure/AWS/GCP) including lakehouse, warehouse, and real-time componentsMetadata, Catalog, and Lineage ArchitectureEnsure scalability, performance, security, and cost optimizationDesign metadata ingestion patterns and lineage frameworks across pipelines, BI tools, and MDM systemsImplement enterprise cataloging solutions using platforms such as Collibra, Atlan, Alation, or similarEnsure metadata is complete, accurate, and actionable for governance and engineering teamsHands-On Technical ExecutionBuild and validate architectural prototypes, POCs, and reference implementationsWrite SQL, design schemas, build lineage connectors, and define transformation logicTroubleshoot complex data architecture issues across pipelines, models, and platformsCross-Functional LeadershipPartner with data engineering, analytics, MDM, governance, product, and application teamsProvide architectural guidance, code reviews, and technical mentorshipCommunicate architectural decisions to executives, engineers, and business stakeholdersYou Must Have8+ years of experience in data architecture, data engineering, or enterprise architectureDeep hands-on experience with cloud data platforms (Snowflake, Databricks, Azure, AWS, or GCP)Strong expertise in data modeling (dimensional, relational, canonical, semantic)Experience architecting MDM and governance solutions using Collibra, Reltio, Atlan, Informatica, or similarStrong SQL, data pipeline design, and metadata/lineage engineering skillsExperience with modern data stack tools (dbt, Spark, Kafka, Airflow, etc.)Ability to translate business needs into scalable architectural designsExperience with enterprise architecture frameworks (TOGAF, DAMA-DMBOK)Background in designing AI-ready data architectures (feature stores, vector stores, semantic layers)Experience with API-driven architectures and event-driven patternsFamiliarity with data products and data mesh conceptsAdoption of standardized data models and architectural patterns across the enterpriseReduction in data duplication, inconsistencies, and integration complexityHigh-quality, governed, discoverable data powering analytics and AIScalable, cost-efficient cloud data platform performanceStrong alignment between business, engineering, and governance teamsWe ValueExperience with enterprise architecture frameworks (TOGAF, DAMA-DMBOK)Background in designing AI-ready data architectures (feature stores, vector stores, semantic layers)Experience with API-driven architectures and event-driven patternsFamiliarity with data products and data mesh conceptsSuccess MeasuresAdoption of standardized data models and architectural patterns across the enterpriseReduction in data duplication, inconsistencies, and integration complexityHigh-quality, governed, discoverable data powering analytics and AIScalable, cost-efficient cloud data platform performanceStrong alignment between business, engineering, and governance teams