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
Given a 500GB transactions table that grows by 50GB per month, design a partitioning and indexing strategy to support fast monthly rollups and avoid full table scans. Explain partition key choice, index types to support common queries, and how you would manage partition lifecycle (retention and compaction).
HardTechnical
0 practiced
Design a monitoring and alerting strategy for data drift in input features used by downstream dashboards and ML models. Define what drift metrics to track, thresholds for alerts, how to surface drift to analysts, and automated remediation options such as retraining or data tagging.
HardTechnical
0 practiced
You suspect that a key KPI calculation in a production dashboard is incorrect. Describe the investigative steps you would take to debug the discrepancy across layers: raw data, ingestion, transformations/models, and dashboard calculation. Include how you would build tests to prevent similar regressions and communicate findings to stakeholders.
MediumTechnical
0 practiced
You need to standardize key analytics metrics such as DAU, MAU, conversion rate, and churn across teams. Propose a metric taxonomy and naming conventions, and explain how you would implement a single source of truth and measure registry to avoid metric divergence.
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