Technical practice
How would you design a React experience for exploring metrics and comparing trends?
What to coverThe posting calls for intuitive, self-service exploration and visualization. Suggested answer focus: explain how users would refine a question, compare consistent metrics, and move between a chart, a table, and the underlying context without losing track of what each view represents.
How would you model and compute a business metric in SQL so different users get consistent results?
What to coverSQL, data modeling, and metrics computation are listed as must-have skills. Suggested answer focus: define the metric’s grain, filters, and time window; then describe validation checks and how you would communicate assumptions.
How would you investigate a change in a time series without overstating its cause?
What to coverThe role involves time series analysis and visualizing trends and causal drivers. Suggested answer focus: discuss baselines, segmentation, and possible confounders, while distinguishing an observed association from evidence of causation.
How would you turn a plain-English question into a grounded agent-assisted analysis?
What to coverThe posting describes pipelines that query, aggregate, and explain data, and lists prompt design, RAG, evaluation, and grounding. Suggested answer focus: outline how you would resolve ambiguity, retrieve relevant definitions or data, check the resulting query and calculations, and limit unsupported claims.
How would you design a FastAPI endpoint for an analytical summary and chart?
What to coverThe role calls for insight APIs, data validation, and asynchronous retrieval. Suggested answer focus: describe request and response contracts, validation, handling slow or failed retrieval, and how the response would remain useful for human review or automation.
What would you expose so a user can validate an AI-generated insight?
What to coverThe posting explicitly asks for underlying queries, data sources, and assumptions to be visible. Suggested answer focus: explain how to present that evidence alongside the answer and what you would do when sources conflict or an assumption cannot be verified.
How would you evaluate whether model-driven insights are analytically correct and useful?
What to coverThe role calls for quality measures such as relevance and latency and mentions evaluations and golden datasets. Suggested answer focus: propose representative questions with expected evidence, checks for numerical correctness and grounding, and a way to review failure cases.
How would you test whether an analytics experience improves decisions rather than just generating answers?
What to coverThe posting names A/B tests as an insight-quality method and describes reduced time-to-decision and greater data confidence as intended outcomes. Suggested answer focus: distinguish product-use measures from answer-quality measures, discuss an appropriate comparison, and note the limits of interpreting either alone.
How would you improve response speed without sacrificing analytical correctness?
What to coverThe intended experience delivers grounded answers within seconds, and the role includes testing and performance monitoring. Suggested answer focus: identify where query, retrieval, API, and model latency arise; explain what you would measure and which optimizations would require correctness checks.
How would you present a complex result as a clear, trustworthy data story?
What to coverThe posting emphasizes charts, tables, contextual narratives, and communication of analytical logic and trade-offs. Suggested answer focus: choose views suited to the question, make comparisons and uncertainty legible, and give users a path from the headline finding to its supporting evidence.
Behavioural practice
Tell me about a time you aligned different teams on a metric or evaluation approach.
What to coverThe role involves partnering with data science, product management, and analytics on metrics and evaluation. Suggested practice: use a real situation, your actions to resolve differing definitions or priorities, and the resulting decision or measurable outcome.
Describe a time user feedback changed an analytical product or workflow.
What to coverThe posting calls for iterating on user feedback in self-service analytics. Suggested practice: describe the original user difficulty, how you investigated and acted on it, and what changed for users afterward.
Tell me about a time you found that an apparently convincing analysis was misleading.
What to coverThe role emphasizes analytical correctness, visible assumptions, and trust in insights. Suggested practice: explain the situation, how you checked the evidence and communicated the issue, and the result without claiming certainty you did not have.
Describe a time you balanced speed of delivery against quality or interpretability.
What to coverThe posting seeks answers within seconds while also calling for tests, monitoring, and clear analytical logic. Suggested practice: give a real example of the trade-off, the safeguards or scope choices you made, and the outcome.
Tell me about a time you turned a recurring analysis request into a reusable experience.
What to coverThe role focuses on moving beyond individual analyses toward AI-first, self-service analytics products. Suggested practice: describe the recurring need, your contribution to making it reusable, and evidence of how the resulting workflow was used or improved.