Data Problem Solving and Business Context Questions
Practical data oriented problem solving that connects business questions to correct, robust analyses. Includes translating business questions into queries and metric definitions, designing SQL or query logic for edge cases, handling data quality issues such as nulls duplicates and inconsistent dates, validating assumptions, and producing metrics like retention and churn. Emphasizes building queries and pipelines that are resilient to real world data issues, thinking through measurement definitions, and linking data findings to business implications and possible next steps.
MediumTechnical
0 practiced
Two analytics tools show different monthly revenue numbers (Tool A: 1.02M, Tool B: 980k). Provide a reconciliation plan: datasets and dimensions to compare, instrumentation and sampling checks, timezone and attribution window differences, and how to present root causes to the business.
EasyTechnical
0 practiced
Given events(event_id, user_id, event_type, event_time) where event_type is in ('page_view','add_to_cart','checkout'), write a SQL query to compute daily conversion rates page_view -> add_to_cart -> checkout for users. Treat a user as converted for a step if they had the subsequent event on the same calendar day.
HardTechnical
0 practiced
Case study: premium plan revenue in Europe fell 15% this quarter. Draft an analysis plan including required data sources, key metrics to compute (cohort LTV, churn, ARPU), segmentation slices, quick exploratory checks, and concrete experiments or product actions you might recommend based on possible root causes.
HardSystem Design
0 practiced
Design a monitoring and alerting framework for a production metrics pipeline. List health metrics to track (ingest rate, late rows, schema changes, duplicate rate), recommend alert thresholds and severity levels, draft on-call runbooks, and propose automated remediation options and executive dashboards.
MediumTechnical
0 practiced
Beta users self-select and appear more engaged. Explain methods to measure and mitigate selection bias when estimating the feature's impact using observational data. Cover propensity score matching, weighting, stratification, and when instrumental variables might be appropriate.
Unlock Full Question Bank
Get access to hundreds of Data Problem Solving and Business Context interview questions and detailed answers.
Sign in to ContinueJoin thousands of developers preparing for their dream job.