Analytical Background Questions
The candidate's analytical skills and experience with data driven problem solving, including statistics, data analysis projects, tools and languages used, and examples of insights that influenced product or business decisions. This covers academic projects, internships, or professional analytics work and the end to end approach from hypothesis to measured result.
EasyTechnical
0 practiced
Explain the difference between a data lake and a data warehouse. For a product analytics team that runs ad-hoc SQL and ML experiments, recommend which to use for different workloads and justify with examples.
HardTechnical
0 practiced
As a staff data engineer, lead a cross-functional analytics project that will influence the product roadmap. Describe your end-to-end approach: framing hypotheses, defining success metrics, instrumenting events, building pipelines, running experiments or observational analysis, handling stakeholder alignment, and delivering a compelling story to executives. Mention how you'd manage trade-offs and timelines.
EasySystem Design
0 practiced
Design a simple star schema for an e-commerce analytics warehouse to support order analytics. Provide a fact table schema and at least three dimension table schemas. Explain partitioning strategy, typical indexes or clustering, and how this design supports common queries (revenue by day, by product category, by user segment).
HardTechnical
0 practiced
The company wants a single source of truth for the revenue metric across analytics, finance, and product. Describe a practical plan to reconcile different definitions, design canonical ETL and governance, ensure reproducible computation, and how to roll out adoption across teams.
MediumTechnical
0 practiced
Write a SQL query to flag anomalies in daily revenue per region using a moving z-score: compute mean and stddev over the previous 30 days (excluding the current day) and flag the day if revenue > mean + 3*stddev. Assume table revenue(region STRING, date DATE, revenue FLOAT). Include handling in SQL for small sample sizes.
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