314,552 interview questions from 6,000+ companies.
Approach for maintaining data quality and integrity across ETL pipelines.
Define a practical KPI set for product success, balancing a north star metric with leading indicators.
Explain a practical approach to user research in the design process, from understanding user needs to turning findings into design decisions.
Explain how to distinguish early directional metrics from outcome metrics, using a clear KPI framework tied to product decisions.
Approach for safely backfilling missing data while preserving correctness, idempotency, and data quality.
Diagnose why conversion fell from 4.8% to 3.1% after a launch by breaking the metric across funnel steps, cohorts, and segments.
Explain what a p-value means in hypothesis testing and how it relates to statistical significance.
Compare ETL and ELT, and explain when ELT is the better pipeline pattern.
Analyze where users drop off in a product funnel and identify the biggest conversion leak.
Explain how to test whether an observed experiment lift is real using hypothesis testing, p-values, and confidence intervals.
Approach for adding data quality checks, observability, and production monitoring to a data pipeline.
Reason about sample size, power, and minimum detectable effect before launching an experiment.
Share how you used data to shape a business decision, including the analysis, recommendation, and outcome.
Explain what drives your best performance and connect it to building useful products for demanding users.
Design a safe backfill for missing customer records after an upstream fix, with idempotent reprocessing and data quality checks.
Explain how to structure a cohort retention query using cohort assignment, period offsets, and aggregation in PostgreSQL.
Framework for deciding whether to ship a feature as an MVP or wait for a more complete launch.
Define how to measure whether a new customer-facing feature will succeed before launch.
Framework for choosing the right primary success metric for a new feature, including leading indicators, guardrails, and business alignment.
Define a North Star Metric for an SMB product that reflects customer value and supports growth decisions.
