Architecture & Strategy. Define and maintain enterprise data architecture and long-term roadmap aligned to business and operational priorities. Design scalable, secure, and high-performing data platforms across cloud and hybrid environments. Establish architectural standards, governance frameworks, and design patterns across the data lifecycle. Evaluate emerging technologies and drive continuous platform innovation. Data Platform & Engineering. Architect and optimize batch and real-time data ingestion and replication processes. Design and oversee transformation frameworks (e.g., dbt) to enable modular, testable, and reusable data models. Integrate data across SAP HANA, SAP Data Sphere, SQL Server, and Saas platforms into governed environments. Lead modernization efforts from legacy systems to cloud-native architectures. Implement CI/ CD and DevOps best practices for data pipelines and analytics workflows. Data Modeling & Performance. Develop and govern conceptual, logical, and physical data models. Apply dimensional and data vault modeling techniques to support enterprise analytics. Optimize platform performance, scalability, and cost efficiency, particularly within Snowflake environments. Analytics Enablement. Partner with BI teams to design semantic layers and curated datasets. Enable scalable analytics through tools such as Power BI and Sigma. Promote self-service analytics through strong data design, governance, and documentation standards. Optimize semantic models, aggregations, and query performance. Governance, Security & Quality. Establish and enforce data governance frameworks, including data quality, lineage, and metadata management. Implement secure, role-based access controls across platforms. Ensure compliance with internal policies and regulatory requirements. Leadership & Collaboration. Translate business and technical requirements into scalable data solutions. Lead architecture reviews and guide enterprise data initiatives. Mentor data engineers and analytics practitioners. Contribute to and support a Data & Analytics Center of Excellence (CoE)AI & Innovation. Leverage AI and automation to improve data quality, pipeline efficiency, and analytics delivery. Identify and implement AI-driven capabilities across the data lifecycle. Promote a culture of innovation, experimentation, and continuous improvement Experience and Qualifications: Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field (Master’s preferred)8 years of experience in data architecture and/or data engineering. Experience designing, implementing, and scaling enterprise-grade data platforms. Experience with data governance, metadata management, and data quality frameworks. Experience implementing CI/ CD and DevOps practices for data workflows. Snowflake or cloud platform certifications. Experience in large, complex, or multi-entity organizations. Experience establishing or contributing to a Data & Analytics CoE - Skills and Physical Demands: Advanced SQL and data modeling (dimensional, normalized, data vault)Expertise in cloud data platforms (e.g., Snowflake or similar)Data integration tools (e.g., HVR, Fivetran, or equivalent)Data transformation frameworks (dbt preferred)Business intelligence tools (Power BI, Sigma, or similar)Data architecture across hybrid cloud and on-prem environments. API-based integrations and/or streaming data architectures. ERP data integration experience (e.g., SAP HANA, SAP Data Sphere)Data governance, lineage, and metadata management tools. CI/ CD pipelines and DevOps practices for data engineering. Strong stakeholder engagement and partnership skills. Ability to communicate complex technical concepts to non-technical audiences. Collaboration across cross-functional and technical teams. Adaptability in a fast-paced, evolving technology environment. Mentorship and team development capabilities