Designing, building, and enabling dashboards and spreadsheet based analysis to turn data into actionable insights for different stakeholder audiences. Candidates should be able to define and prioritize key performance indicators and metrics for roles such as sales, marketing, finance, and executives; apply dashboard design principles that present complex data clearly; and enable self service analytics through reusable data models, standardized metrics, documentation, and user training. Practical spreadsheet skills are included: advanced formulas, pivot tables, lookup functions, data cleaning, filtering, charting, sensitivity and what if analysis, and performance optimization. Candidates should also speak to tools and platforms used such as Excel, Google Sheets, business intelligence platforms, visualization tools, and analytics platforms; consider refresh cadence, data validation and governance, interactivity and drill down patterns, and trade offs between standardized reporting and bespoke custom views.
EasyTechnical
0 practiced
Given a table named `sales` with columns: sale_id (PK), product_id, category, amount numeric, occurred_at timestamp with time zone. Write an ANSI/PostgreSQL SQL query to compute monthly revenue by product category for the last 12 months. The output should be: year, month, category, revenue. Include months with zero revenue per category (i.e., fill gaps). Explain how you handle timezones, calendar generation, and assumptions about missing category data.
EasyBehavioral
0 practiced
Tell me about a time you built a dashboard that directly influenced a high-stakes business decision. Use the STAR format: describe the Situation and Task, explain the Actions you took (data prep, validation, visualization choices), and quantify the Results. Emphasize stakeholder engagement and how you measured impact after the decision.
HardTechnical
0 practiced
Design a continuous integration (CI) and automated testing strategy for BI artifacts: SQL models, metrics, dashboards, and data pipelines. Include unit testing for SQL models, metric sanity checks, smoke tests for dashboard endpoints, visual regression testing, test data generation, and how to integrate tests into PR workflows.
EasyTechnical
0 practiced
Explain considerations for dashboard refresh cadence: compare real-time (sub-second to seconds), near-real-time (minutes), hourly, and daily refresh strategies. Discuss trade-offs in data freshness, cost, load on data systems, complexity of pipelines, and recommended cadences for executive finance dashboards versus operational KPI monitoring.
MediumTechnical
0 practiced
Explain how you would implement row-level security (RLS) in Tableau and in Power BI for a global sales organization where each salesperson must only see their territory. Discuss static vs dynamic approaches, performance implications, maintainability, and auditability. Provide an example mapping approach using a user-to-territory table.
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