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
For a revenue metric with a long-tailed distribution, describe how you would surface a representative central tendency on a dashboard and explain multiple approaches (mean, median, trimmed mean, winsorizing, log-transform). Discuss tradeoffs and how you would present both raw and adjusted metrics to stakeholders.
EasyTechnical
0 practiced
You observe a one-day spike in signups on March 1. List the SQL checks and data validations you would run to determine whether the spike is a true business event or a data ingestion/duplication error. Provide example SQL snippets or steps you would run against event and ingestion logs.
HardTechnical
0 practiced
Given a query that joins a 500M row events table with a 50M users table and aggregates daily counts, outline concrete optimization strategies for modern warehouses (BigQuery, Snowflake, Redshift) and engines (Spark). Discuss partitioning, clustering/sorting keys, materialized views, pre-aggregation, and trade-offs between storage and compute costs.
HardBehavioral
0 practiced
A stakeholder asks you to change the definition of 'active user' from 7-day to 14-day to make engagement look better but does not want the change documented. How would you respond? Describe the ethical, technical, and governance steps you would take to handle this request, including how to version metric definitions and communicate changes.
HardTechnical
0 practiced
Write production-quality SQL to compute rolling 30-day retention cohorts where 'active' is defined as either 'login' or 'purchase' events. Handle deduplication, late-arriving events, and users with multiple identities merged post-hoc. Also describe how you would test and validate the output against a small hand-crafted sample.
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.