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
0 practiced
You need to compare two candidate ETL implementations: a pure SQL approach using incremental MERGE into a data warehouse versus a Python-based pipeline that performs complex transformations before loading. For a data volume of 20M rows per day and transformations that include joins, deduplication, and enrichment, discuss cost, maintainability, testability, and performance trade-offs and recommend one approach with justification.
MediumTechnical
0 practiced
You maintain a BI codebase with SQL models and Python scripts. Propose a git-based workflow to manage changes to SQL models, dashboard updates, and deployment to production. Include branching strategy, code review requirements, and automated tests or linting you would enforce before merging.
HardTechnical
0 practiced
You are given a slow SQL query that performs multiple joins and grouping over a 2TB fact table. The query currently uses DISTINCT and multiple subqueries. Rewrite or propose a refactor that improves performance using techniques like window functions, materialized aggregates, or join order changes. Explain why your refactor should be faster and how you would test it.
HardTechnical
0 practiced
You have a Python ETL job that reads 100M rows, groups by several keys with window functions, and currently consumes all memory and crashes. Describe concrete code changes and architecture-level adjustments to make this pipeline memory-efficient: streaming, chunked aggregation, use of SQL pushdown, or distributed processing. Provide code sketch or pseudocode.
EasyTechnical
0 practiced
You are responsible for daily ETL that loads sales data. List a lightweight checklist of automated data quality checks you would run after each load to ensure completeness and freshness. Include SQL examples or brief descriptions for checks like row count, null ratios on key fields, and referential integrity, and describe how you would notify stakeholders when checks fail.
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