Business Intelligence Data Architect

About the role Defines and governs enterprise data architecture supporting business intelligence, applications, automation, and artificial intelligence. Establishes scalable, secure, and cost-effective patterns for data ingestion, integration, storage, modeling, governance, metadata, lineage, and consumption. Evaluates source systems, data flows, platforms, and technical requirements and translates them into target-state architectures, standards, and roadmaps. Provides architectural direction so data is trusted, accessible, reusable, compliant, and ready for future business needs. Key responsibilities Define enterprise data architecture, target-state designs, standards, and roadmaps across ingestion, integration, storage, transformation, analytics, application, and AI layers. Establish data models, canonical definitions, metadata, lineage, master/reference data, and semantic patterns that promote consistency and reuse. Define architectures for data pipelines, APIs, warehouses/lakehouses, cloud platforms, and structured and unstructured data. Embed data governance, quality, security, privacy, access, retention, resiliency, and regulatory requirements into architectural standards. Evaluate platforms and data sources, conduct design reviews, document decisions, and guide teams on scalability, performance, and cost tradeoffs. About you Can work in a Hybrid environment and in a Night-shift schedule Bachelor's degree in Computer Science, Information Systems, Engineering, Data Management, or related field; equivalent relevant experience may be considered. Strong leadership and management skills Experience in data architecture, data engineering, enterprise data platforms, or related disciplines, including experience designing enterprise-scale data solutions. Knowledge of enterprise data architecture, data modeling, integration patterns, metadata and lineage, data governance, security, cloud data architecture, and analytical/semantic design. Experience with cloud data platforms, data warehouses/lakehouses, SQL, data modeling tools, ETL/ELT and orchestration technologies, APIs, and BI/analytics environments. Familiarity with programming, automation, and AI/ML data requirements preferred. Strong architecture, problem-solving, communication, and documentation skills; ability to establish standards and influence technical and business stakeholders.