Expect a mix of hands-on technical prompts, structured business cases, and behavioral questions that probe collaboration and results. The examples below are representative of what you’ll face, synthesized from candidate reports and typical BA assessments for marketplace analytics.

Technical / SQL

Questions test your ability to translate business prompts into accurate queries.

• Write a query to compute weekly active sellers and their share of total GMV.
• Calculate listing-to-purchase conversion by category, filtering out self-purchases.
• Identify cohorts of new buyers and compute 30-day retention with window functions.
• Find duplicate orders and explain how you’d de-duplicate reliably.
• Given event logs (view, add-to-bag, purchase), compute stepwise funnel conversion.

Statistics / Experimentation

You’ll design tests, assess validity, and make ship/no-ship calls.

• How do you determine sample size for a 1% detectable lift at 80% power?
• An A/B shows p=0.07 but strong directional lift—what do you recommend and why?
• Explain Type I vs. Type II error using a marketplace promotion example.
• How do you handle seasonality when testing a weekend-heavy feature?
• Describe a time when peeking led to a false conclusion. How would you prevent it?

Business Case / Marketplace Analytics

Cases assess structure, metrics, and decision-making under ambiguity.

• GMV is flat despite traffic growth—diagnose the issue and propose actions.
• Cancellations spike in apparel after a shipping change—what’s your analysis plan?
• Prioritize between acquiring new sellers vs. reactivating dormant sellers.
• Design a dashboard to monitor marketplace liquidity and health.
• Recommend metrics to evaluate a new seller education program.

Behavioral / Leadership

Expect STAR-driven stories that demonstrate influence and outcomes.

• Tell me about a time you drove a decision that others disagreed with—what happened?
• Describe a project where you built a metric framework that changed behavior.
• How do you handle conflicting definitions of “active user” across teams?
• Share a failure in an analysis and how you corrected course.
• How do you ensure stakeholders take action on your insights?

Product Analytics / Metrics Design

You’ll define success and set up measurement foundations.

• What is the primary metric for a new search relevance improvement? Guardrails?
• How would you measure the impact of social sharing features on listing velocity?
• Propose a north-star metric and supporting inputs for seller success.
• How do you detect metric drift and address data quality issues?
• Outline the MVP analytics for a new buyer onboarding flow.

Deep Dive into Evaluation Areas

SQL and Data Fluency

SQL is your primary tool for extracting and analyzing marketplace data. Expect live coding or collaborative query-writing focused on correctness, readability, and performance. Interviewers want to see you translate a business question into a query plan and verify results against edge cases.

• Be ready to go over:

  • Joins and Window Functions: Ranking sellers, computing rolling retention, and funnel step conversions
  • Aggregations and Filtering: Daily active buyers, repeat purchase rate, and item-level conversion
  • Data Quality and Validation: Duplicates, null handling, timezone boundaries, and metric reconciliation
  • Advanced concepts (less common): CTE structuring for complex cases, query optimization trade-offs, segmentation at scale

• Example questions or scenarios:

  • "Write a query to compute 7-day buyer retention by acquisition channel using event data."
  • "Find the top 10 categories by GMV growth month-over-month; handle new category introduction."
  • "Given orders, users, and listings tables, calculate conversion from listing view to purchase, segmented by device."

Statistics and Experimentation

You’ll apply practical statistics to evaluate features, campaigns, and marketplace policies. Interviewers look for sound experimental design, clear assumptions, and crisp interpretation of results.

• Be ready to go over:

  • Hypothesis Testing: Null/alternative, p-values, Type I/II errors, and power
  • A/B Testing Mechanics: Sample size, randomization, guardrail metrics, and CUPED/baseline adjustment
  • Interpretation & Pitfalls: Peeking, novelty effects, non-independence, and seasonality
  • Advanced concepts (less common): Heterogeneous treatment effects, sequential testing, quasi-experiments

• Example questions or scenarios:

  • "Design an A/B test for a new push notification to increase listing shares; define primary/guardrail metrics."
  • "Experiment shows +1.5% conversion (p=0.06). Ship or not? What additional evidence would you seek?"
  • "How would you estimate LTV impact of a new shipping promotion when a clean A/B isn’t feasible?"

Business and Marketplace Problem-Solving

Poshmark is a two-sided social marketplace; strong answers demonstrate understanding of supply-demand dynamics, trust/safety, and network effects. Interviewers will test how you structure problems and quantify trade-offs.

• Be ready to go over:

  • Funnel & Cohorts: Acquisition to first listing/purchase; repeat purchase cadence; churn signals
  • Marketplace Health: Fill rate, time-to-sell, inventory liquidity, take rate, and returns
  • Growth & Monetization: Promotion mechanics, fee changes, and seasonal campaigns
  • Advanced concepts (less common): Elasticity modeling, marketplace incentives, cross-side externalities

• Example questions or scenarios:

  • "Listings grew 10%, but GMV is flat. Diagnose and propose next steps."
  • "Cancellations spiked in one vertical; outline a rapid analysis and action plan."
  • "Prioritize between improving search relevance vs. accelerating shipping—how do you decide and measure?"

Communication and Influence

Your impact depends on how well you drive decisions through storytelling and stakeholder alignment. Interviewers assess brevity, clarity, and your ability to move from data to recommendation.

• Be ready to go over:

  • Executive Summaries: Clear problem, key insight, decision, and expected impact
  • Stakeholder Alignment: Resolving metric disputes, creating shared definitions, and SLAs
  • Visualization & Dashboards: Metric design, alerting, and actionability
  • Advanced concepts (less common): Building decision frameworks, scenario planning, counterfactual narratives

• Example questions or scenarios:

  • "Walk me through a dashboard you built that changed a team’s behavior—what decisions did it enable?"
  • "Tell me about a time stakeholders disagreed—how did data resolve the impasse?"
  • "Present a 3-slide summary of a complex analysis and request a decision."