Data Analyst Interview Questions

Showing 1–30 of 32 results

• AffirmMedium·Data Analyst
  Choose the Next Product or Vertical for a Lending Business

  Compare lending product and vertical investments using demand, contribution economics, credit risk, execution capacity, scenarios, and bounded pilots.

  Product / Decision MakingTechnical Screen
• AffirmMedium·Data Analyst
  Connect Analyst Experience, Delivered Insights, and Your Next Role

  Practice connecting relevant analyst work, a concrete delivered insight and the responsibilities you want in a credit-risk role.

  Behavioral & LeadershipTechnical Screen
• AffirmMedium·Data Analyst
  Investigate Rising Loan Delinquency with Stable Approval Rates

  Diagnose rising 30-day loan delinquency, separate borrower-mix shifts from model drift, and evaluate merchant underwriting changes.

  Analytics & ExperimentationTechnical Screen
• AffirmMedium·Data Analyst
  Investigate a Quarterly Increase in Loan Delinquency

  Investigate rising delinquency through rate definitions, reporting checks, loan vintages, seasoning, portfolio mix, and within-segment deterioration.

  Analytics & ExperimentationTechnical Screen
• AffirmMedium·Data Analyst
  Present a Relevant Analytics Project to a Hiring Manager

  This question tests your ability to structure a past data project using the STAR method, with a specific focus on technical rigor, business logic, and self-reflection.

  Behavioral & LeadershipTechnical ScreenBehavioralAnalyticsStorytelling
• Capital OneEasy·Data Analyst
  Analyze Mission Outcomes and Allocate Response Units

  Analyze incident response records to calculate unit win rates, evaluate the impact of rest periods on success, and rank response units using tie-breakers.

  Data Manipulation (SQL/Python)Online AssessmentPythonData ManipulationAnalytics
• Capital OneMedium·Data Analyst
  Compare Two-Year Economics for Renewing or Replacing a Show

  Calculate two-year show profits, derive the success-probability break-even point, and evaluate renewal and profit-improvement choices.

  Analytics & ExperimentationTechnical Screen
• Capital OneMedium·Data Analyst
  Set a Minimum Sale Price After Audience Loss and Reinvestment

  Calculate a show’s minimum sale price using lost audience value and reinvestment profit, then assess a 60-million-dollar offer.

  Analytics & ExperimentationTechnical Screen
• Capital OneEasy·Data Analyst
  Should a Restaurant Partner with Groupon?

  This question evaluates a candidate's competence in unit economics, break-even and profitability modeling, and the ability to distinguish incremental versus cannibalized customers when assessing voucher-based promotional partnerships, reflecting skills in revenue modeling, cost accounting, and business analytics.

  Analytics & ExperimentationTechnical Screen
• EEarninMedium·Data Analyst
  Defend the Decisions Behind a High-Impact Data Project

  Prepare a senior project deep dive connecting a real business decision to your analytical choices, alternatives, validation, influence, and measured outcome. The exercise helps you show ownership, technical judgment, honest attribution, and reflection under probing follow-ups.

  Behavioral & LeadershipTechnical Screen
• EEarninMedium·Data Analyst
  Explain an Innovative Analytical Method You Introduced

  Practice presenting an innovative analytics method as a decision-focused story: identify the weak baseline, explain your contribution and tradeoffs, validate the improvement fairly, earn stakeholder adoption, and discuss measurable impact and lessons.

  Behavioral & LeadershipTechnical Screen
• EEarninMedium·Data Analyst
  Resolve a Cross-Functional Data Disagreement

  Prepare for senior behavioral questions about cross-functional conflict by separating evidence, goals, constraints, and decision rights. Practice building shared criteria, proposing safe tests, communicating uncertainty, and owning execution after alignment.

  Behavioral & LeadershipTechnical Screen
• GoogleHard·Data Analyst
  Balance Fraud Control with User Experience

  Practice designing fraud controls that balance expected harm, risk-based verification, legitimate-user friction, and reliable policy evaluation.

  Product / Decision MakingOnsite
• GoogleHard·Data Analyst
  Choose a North Star Metric for a Malicious-Webpage Classifier

  Choose and measure a malicious-webpage classifier metric using exposure-weighted recall, false-warning guardrails, and unbiased label audits.

  Analytics & ExperimentationOnsite
• GoogleHard·Data Analyst
  Design a Spam Review Workflow with Classifiers, AI Agents, and Human Labelers

  Design a spam review workflow combining classifiers, AI agents, and human labelers with accuracy, throughput, and label-quality evaluation.

  ML System DesignOnsite
• GoogleHard·Data Analyst
  Investigate a Decrease in Webpage Warnings

  Diagnose falling malicious-webpage warnings by decomposing traffic, scoring, flags, rendering, and labels to separate improvement from failure.

  Analytics & ExperimentationOnsite
• GoogleHard·Data Analyst
  Investigate a Drop in Sales

  Use a structured sales-drop investigation covering metric definitions, data quality, funnel decomposition, segmentation, and causal evidence.

  Analytics & ExperimentationOnsite
• HDHome DepotMedium·Data Analyst
  Debug row loss after SQL joins

  This question evaluates SQL data manipulation and JOIN logic skills, including temporal and timezone-aware date handling and aggregation, within the Data Manipulation (SQL/Python) domain and primarily tests practical application-level competency.

  Data Manipulation (SQL/Python)Onsite
• LLumenMedium·Data Analyst
  How would you handle common analyst workplace scenarios?

  This question evaluates prioritization, stakeholder management, written and executive communication, ownership, collaboration, and inclusive teamwork competencies for a Data Analyst within a Behavioral & Leadership context.

  Behavioral & LeadershipOnline Assessment
• NNatooraMedium·Data Analyst
  Design a 25,000-CSV ETL pipeline

  This question assesses the ability to design a robust, scalable ETL architecture capable of processing large volumes of semi-structured data while ensuring data integrity and operational reliability.

  System DesignTechnical ScreenETLData EngineeringData Quality
• NNatooraMedium·Data Analyst
  Explain pricing fit and ETL architecture

  A Natoora Data/Pricing Analyst technical screen that probes how a candidate with no direct pricing experience defends their fit: transferable analytics skills, a ramp plan on food-pricing metrics (revenue, margin, volume, markdown, spoilage), Google Sheets vs code trade-offs, and an end-to-end ETL pipeline deep dive. The model answer shows how to articulate data infrastructure, automation levels, and decision points credibly.

  Behavioral & LeadershipTechnical Screen
• NNatooraMedium·Data Analyst
  How adapt to Google Sheets?

  This question evaluates the candidate's ability to transfer core analytics skills to a spreadsheet-first workflow, adapt to collaborative and audit-sensitive data processes, and demonstrate leadership around governance, scalability, and the downstream impact of pricing decisions.

  Behavioral & LeadershipTechnical Screen
• NNatooraMedium·Data Analyst
  When use LLMs for reporting?

  This question assesses the architectural trade-offs, reliability concerns, and suitability of using LLMs for automated business reporting tasks compared to traditional deterministic methods.

  Machine LearningTechnical ScreenLLMRAGReporting
• NNatooraMedium·Data Analyst
  Why use LLMs for daily summaries?

  This question evaluates a candidate's ability to justify tooling and architecture choices for automated daily summaries, comparing LLM-based natural language generation with rules-based or templated alternatives while accounting for data sources, consumer intent, validation, failure modes, and cost/latency trade-offs.

  Machine LearningTechnical Screen
• PinterestEasy·Data Analyst
  Compute percent of first-cancel users who never return

  This question evaluates a candidate's competency in data manipulation and temporal analysis using SQL/Python, focusing on computing user-level metrics and correctly ordering time-based events.

  Data Manipulation (SQL/Python)Technical Screen
• PinterestMedium·Data Analyst
  Compute percent of first-cancelled users who never rebook

  This question evaluates proficiency in SQL data manipulation—specifically use of window functions, CTEs, deduplication by earliest timestamp, temporal comparisons, and cohort-level aggregation—within the Data Manipulation (SQL/Python) domain for a Data Analyst role in health‑tech product analytics.

  Data Manipulation (SQL/Python)Technical Screen
• RevolutHard·Data Analyst
  Define Ultra success and detect suspicious transactions

  This question evaluates a candidate's skills in product analytics, cohort and retention measurement, SQL-based metric design and segmentation, and rule-based fraud detection using transactional and activity data.

  Analytics & ExperimentationTechnical Screen
• RipplingMedium·Data Analyst
  Find Payroll Bookings in the Latest Active Month by Country

  Tests PostgreSQL date manipulation, filtering, and aggregation by determining the most recent month with active payroll bookings and summarizing totals by country.

  Data Manipulation (SQL/Python)Technical Screensqlpostgresqldate-manipulation
• StripeMedium·Data Analyst
  Evaluate Stripe Capital Loan Performance

  This question evaluates a data analyst's competency in product analytics, metric design, cohort and segment analysis, and causal inference for a working-capital loan product.

  Analytics & ExperimentationTechnical Screen
• TNThe New York TimesEasy·Data Analyst
  Write SQL for content-view analytics

  This question evaluates proficiency in SQL-based data manipulation and analytics, including aggregation, distinct counting, NULL handling, timestamp/hour extraction, device grouping, and row-level ordering, and is situated in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen