Data Analysis and Requirements Translation Questions
Focuses on translating ambiguous business questions into concrete data analysis plans. Candidates should identify the data points required, define metrics and key performance indicators, state assumptions to validate, design the analysis steps and queries, and explain how analysis results map back to business decisions. This includes data quality considerations, required instrumentation, and how analytical findings influence product requirements or architectural choices.
MediumSystem Design
0 practiced
Describe how you would design platform features to enable reproducible analyses: capture immutable raw datasets, version transformation code and SQL, snapshot intermediate artifacts, store environment metadata, and present lineage. Explain how analysts can re-run historical analyses deterministically and what trade-offs exist around storage and retrieval costs.
HardTechnical
0 practiced
Case study: Product leadership asks to 'increase engagement' within the quarter. As a data engineer, propose a measurable framework that maps this vague goal into leading and lagging metrics, an instrumentation plan for those metrics, an experiment and rollout strategy, and a dashboarding/reporting plan. State assumptions, risks, and trade-offs you would communicate to leadership.
EasyBehavioral
0 practiced
Tell me about a time you received an ambiguous analytics request from a PM (e.g., "improve engagement"). Describe the situation, how you clarified the request, the measurable metrics you proposed, the instrumentation or ETL you implemented, and the outcome. Focus on your role in translating ambiguity into concrete data requirements and delivery.
HardTechnical
0 practiced
Propose a canonical, repeatable process that maps high-level business goals to operational KPIs. Include who should participate (PM, data scientist, data engineer, legal), artifacts to produce (metric spec, instrumentation checklist, dashboards), decision gates (experiment vs immediate rollout), and guardrails to avoid metric manipulation or conflicting incentives.
MediumTechnical
0 practiced
Given events(user_id bigint, ip varchar, user_agent varchar, event_time timestamp), write ANSI SQL (or describe sequence of queries) to surface probable bot traffic. Propose heuristics (high events/min per user, many users sharing same user_agent, impossible geo hops) and how you'd validate false positives with sampling and user lookups.
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