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 are advising a small startup (2 analysts) vs. an enterprise (200 analysts). For ad-hoc analysis and for production reporting, which tools would you recommend (SQL, Python, Tableau, Power BI, dbt, etc.) and why? List selection criteria (team skills, cost, speed to insight, governance, scalability) and provide a recommended stack for each scenario.
HardTechnical
0 practiced
Design SLOs and an alerting strategy for feature data used by ML models and downstream dashboards. Define meaningful SLOs for freshness, completeness, and distribution drift of features, describe how to measure and detect drift (statistical tests or distance metrics), and explain alerting/mitigation steps when SLOs are violated.
HardSystem Design
0 practiced
Design a CI/CD pipeline for data transformations (SQL/dbt and Python) that includes: unit tests, integration tests, data quality tests, promotion across dev→staging→prod, and rollback capability. Include tooling choices, branching strategy, test types, and how you'd surface failures to owners.
EasyTechnical
0 practiced
You have a sales CSV with columns: date, region, product, units_sold, revenue. Describe step-by-step how you would build an Excel pivot table to show revenue by region and product, filter to the top 3 regions by revenue, add a quarterly slicer, and calculate YoY percentage change in revenue inside the pivot. Include which Excel features or formulas you'd use.
HardSystem Design
0 practiced
Design a near-real-time analytics pipeline to provide dashboards with <5 minute freshness. Describe choices for ingestion (Kafka/Kinesis), stream processing (Flink/ksqlDB/ Spark Structured Streaming), intermediate storage (materialized views, OLAP DB like Druid/ClickHouse, or Snowflake with streams/tasks), exactly-once or at-least-once semantics, backpressure handling, and how dashboards will query the results.
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