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Enterprise Data Architect
4 weeks ago
Key Responsibilities:
- Lead Data Architecture
- Design, develop, and implement comprehensive enterprise data architectures, primarily leveraging Azure and Snowflake platforms.
- Data Transformation & ETL
- Oversee and guide complex data transformation and ETL processes for large and diverse datasets, ensuring data quality, consistency, and performance.
- Customer-Centric Data Design
- Design and optimize datasets sourced from CRM, Call Center, Marketing, Offline, and Point of Sale systems to meet customer-centric business needs.
- Data Modeling
- Drive the development and maintenance of relational, dimensional, columnar, and big data models to support both operational and analytical use cases.
- Query Optimization
- Develop and troubleshoot complex SQL and NoSQL queries to ensure optimal performance and data accessibility.
- Data Warehouse Management
- Apply best practices in data warehousing to build and manage scalable, high-performing warehouse environments.
- Tool Evaluation & Implementation
- Evaluate, recommend, and implement industry-standard ETL tools such as Informatica and Unifi to align with enterprise data strategy.
- Business Requirements & Analysis
- Lead structured analysis, use case documentation, and process design to translate business requirements into scalable data solutions.
- Reporting & Analytics Support
- Provide architectural guidance to reporting teams using tools like Tableau and Power BI.
- Software Development Practices
- Apply standard software engineering practices to data solutions, ensuring code quality, version control, and documentation.
- Stakeholder Collaboration
- Collaborate with internal sales teams and engage directly with clients to understand their data challenges and deliver strategic outcomes.
- Project Management & Multi-tasking
- Manage and prioritize multiple concurrent projects, demonstrating strong organizational skills and the ability to meet tight deadlines.
- Strategic Thinking & Leadership
- Lead continuous improvement initiatives, staying proactive, self-managed, and focused on driving business value through data architecture.