I fumbled this a bit because I jumped straight into tool names instead of talking about what story the data needs to tell first.
Start by clarifying the business goal and audience for the visualization, then describe how you would model the data (e.g., star schema) and choose appropriate chart types. Emphasize an iterative approach: prototype, gather feedback, and refine, while leveraging tools like Amazon QuickSight or open-source libraries.
Pro tip: Show that you understand the trade-offs between real-time and batch processing, and mention how you would ensure data quality and governance. Also, highlight the importance of aligning metrics with business KPIs and making the visualization actionable.
Ask questions to understand the audience, key metrics, and decisions the visualization should support. Identify data sources and refresh frequency.
Propose a dimensional model (e.g., star schema) with fact and dimension tables to enable efficient slicing and dicing. Consider pre-aggregation for performance.
Choose chart types that best convey the insights: time series for trends, bar charts for comparisons, heatmaps for correlations, etc. Ensure clarity and avoid clutter.
Build a prototype using a BI tool (e.g., QuickSight) or code (e.g., D3.js, Matplotlib). Gather feedback from stakeholders and refine the design.
Address data pipeline scalability, security, and access control. Set up monitoring for data quality and usage metrics of the dashboard itself.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.