General Technical Tool Proficiency Questions
Familiarity and practical experience with technical productivity and analysis tools such as SQL, Python or R, data visualization platforms like Tableau and Power BI, Excel, and statistical or analytical software. Candidates should be able to describe depth of expertise, typical use cases, examples of real world applications, automation or scripting practices, and how they select tools for different problems. This topic includes discussing reproducible workflows, data preparation and cleaning, visualization best practices, and integration of tools into cross functional projects.
MediumTechnical
71 practiced
In Power BI write a DAX measure (explain in pseudocode or DAX) to calculate month-over-month percentage growth in revenue while handling months with zero revenue gracefully. Explain how you would validate the measure across edge cases and different aggregation levels.
EasyTechnical
76 practiced
You're delivering dashboards in Tableau or Power BI. Explain how you'd set up user access and row-level security so managers see only their region's data. Describe how this impacts data source configuration, extracts vs live connections, and dashboard performance.
HardSystem Design
61 practiced
Design a metadata and lineage solution that captures dataset lineage across SQL transformations, Python scripts, and Airflow DAGs to enable impact analysis and data discovery. Recommend open-source tools to use, where to instrument pipelines to capture lineage, and how to present lineage in a catalog UI.
MediumTechnical
56 practiced
Create a plan for a KPI dashboard showing daily active users (DAU), retention curves, and cohort analysis for product growth. Describe recommended chart types, layout and navigation, interactive filters, and data preparation steps such as aggregation frequency, smoothing, cohort definitions, and sample-size considerations before building in Tableau or Power BI.
EasyTechnical
55 practiced
A stakeholder sends an Excel workbook with raw transactions. Describe when you'd use a PivotTable versus Power Query (Get & Transform) versus exporting to a database for analysis. Include pros and cons, repeatability considerations, and typical steps for each approach.
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