Assistant Data Architect

1.Data Analytics Solutions Development 2.Data Integration and Pipeline Management 3. AI Contributions and Emerging Technologies 4. Data Architecture and Modeling 5. Data Strategy and Process Optimization 6. Cross-Functional Collaboration 7. Documentation and Training

1. Data Analytics Solutions Development
• Collaborate with cross-functional teams to understand reporting needs and analytics goals.
• Design and develop dashboards (e.g., referral tracking, finance analytics) to replace manual workflows with real-time insights.
• Perform proof-of-concept work (e.g., sales analysis) to demonstrate the value of data-driven decision-making.

2. Data Integration and Pipeline Management
• Design and maintain data pipelines to consolidate information from multiple sources into a unified data environment.
• Collaborate with stakeholders to define data integration needs, ensuring scalability and reliability of solutions.

3. AI Contributions and Emerging Technologies
• Support AI initiatives (e.g., generative AI integration, chatbot development) by helping build or refine underlying data structures.
• Work with tools like Microsoft Copilot Studio to automate processes, enhance analytics capabilities, and demonstrate the power of generative AI.
• Stay updated on emerging AI and data technologies, recommending best-fit solutions to the organization.

4. Data Architecture and Modeling
• Contribute to the design of robust data architectures to support current and future analytics needs.
• Develop and maintain data models that align with business requirements, ensuring efficient storage and retrieval.
• Collaborate with Architect Lead to define and refine data standards, naming conventions, and documentation.

5. Data Strategy and Process Optimization
• Participate in forming and executing the overall data strategy, aligning technical solutions with business objectives.
• Identify and implement best practices for data lifecycle management, including transformation, storage, and governance.

6. Cross-Functional Collaboration
• Work closely with finance, sales, operations, and other teams to gather requirements and translate them into technical solutions.
• Liaise with business stakeholders to ensure data solutions meet performance, quality, and usability requirements.
• Provide technical guidance on data-related projects to non-technical stakeholders, facilitating effective communication.

7. Documentation and Training
• Develop and maintain comprehensive documentation for data flows, models, and technical processes.
• Provide training and support to end-users and junior data professionals on new data tools, dashboards, and methodologies.
• Share knowledge of new features and functionalities with the broader team, fostering a culture of continuous learning.