Data Architect

You will be responsible for the end-to-end architectural vision, aligning Snowflake's capabilities with the enterprise-wide goal of creating a single source of truth. This role requires a high-level strategist capable of translating complex business requirements into scalable, secure, and actionable technical roadmaps that support analytics and retail services.

Enterprise MDM Strategy & Design Architectural Vision: Lead the long-term evolution and maintenance of the Snowflake cloud platform to support enterprise-level MDM implementation. Conceptual to Physical Modeling: Design complex data schemas (Star/Snowflake) and master data domains that unify fragmented data from sources like Salesforce. Requirement Transformation: Act as the primary technical bridge to BI, Retail Services, and cross-functional teams to elicit high-level business requirements. Actionable Roadmapping: Convert enterprise goals into a prioritized backlog of technical tasks and architectural blueprints for the implementation team.

Global Access Strategy: Configure and manage enterprise-grade security, including advanced Role-Based Access Control (RBAC) and data masking within Snowflake. Compliance Framework: Implement and enforce security best practices to meet strict global compliance and data privacy requirements. Optimization Oversight: Manage and optimize the Snowflake environment to ensure high-performance delivery for business intelligence initiatives.

Ecosystem Alignment: Oversee the integration of the complete data analytics stack, ensuring seamless flow between Snowflake, Python-based automation, and Tableau. Advanced Troubleshooting: Serve as the final escalation point for complex technical issues and data stack downtime. Technical Leadership: Guide and mentor junior to mid-level developers on data warehousing concepts and database design.

Experience: Minimum 7+ years of hands-on experience in Snowflake or comparable cloud-based data platforms. Education: Bachelor's degree in Information Technology, Computer Science, or a related field. Mastery: Expert-level understanding of data warehousing concepts, MDM lifecycle, and database design.

Data Engineering: Expert-level SQL and Python for building automated, scalable data pipelines. Visualization: Advanced proficiency in Tableau to translate raw data into actionable insights. CRM Systems: Deep familiarity with data structures and extraction methodologies.

Strategic Problem-Solving: Ability to resolve systemic, multi-layered technical challenges. Collaborative Leadership: Effective communication skills to align diverse stakeholders with the MDM strategy.

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