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Built by a hiring manager who's conducted 1,000+ interviews at Google, Amazon, Nvidia, and Adobe.
Built by a hiring manager who's conducted 1,000+ interviews at Google, Amazon, Nvidia, and Adobe.
Practice the real Data Analyst questions Jane Street asks, out loud, and get your interview readiness score. Everything you need to prepare is below.
Free to start, no credit card. Interview formats vary by team, level, and location — use this guide as preparation, not a guaranteed sequence.
A practical preparation outline based on commonly reported stages. Your actual process may differ.
A timed assessment covering probability, combinatorics, mental math, sequences, and logical reasoning. Questions are challenging and require both accuracy and speed. This is a highly selective filter.
Key frameworks and strategies for Data Analyst interviews.
Structure answers with Situation, Task, Action, Result. Describe the business problem (15%), your analytical approach and tools (35%), data insights and visualizations created (30%), and business impact with quantified outcomes (20%). Always include specific metrics.
Use these 44 prompts to prepare clear examples. They support practice and are not a claim that every question is asked by Jane Street.
Use SUM with GROUP BY, date filtering with WHERE or HAVING, ORDER BY DESC with LIMIT. Discuss JOIN strategies if customer data is in separate tables. Show understanding of date functions (DATE_SUB, INTERVAL) and handling NULL values.
Align your answers with Jane Street's core values.
Jane Street is a community of deeply curious people who love solving hard problems. Demonstrate genuine love for mathematics, puzzles, and intellectual exploration beyond what is required.
Jane Street emphasizes collaborative thinking over individual brilliance. Show how you work through problems with others, share ideas openly, and build on teammates' contributions.
Practical tips to focus your preparation.
Probability is the foundation of Jane Street interviews. Be fluent in conditional probability, Bayes' theorem, expected value, variance, and common distributions. Practice solving novel probability problems — Jane Street creates new puzzles regularly, so memorization will not help.
Jane Street expects fast, accurate mental arithmetic. Practice daily with multiplication, division, percentages, and estimation. Learn techniques like difference of squares, anchoring, and chunking. Speed and accuracy both matter — interviewers time you informally.
Compare Data Analyst interviews across companies
One to two phone interviews focused on probability, expected value calculations, and mental math. You may encounter trading-style questions where you must quote prices and manage risk in hypothetical markets. Interviewers assess both your answers and your reasoning process.
A full-day on-site consisting of five to seven interviews and activities. Includes probability and math problems, a mock trading session, a behavioral interview, and lunch with the team. The trading simulation evaluates market-making intuition, risk management, and performance under pressure.
The interview panel reviews all rounds and discusses candidates thoroughly. Jane Street's hiring bar is extremely high and requires strong consensus. Successful candidates receive an offer with details on role and competitive compensation.
Phone Screen (30-45 min): SQL basics, data analysis philosophy, tool proficiency Technical Round 1 (60 min): Live SQL coding, query optimization, data manipulation Technical Round 2 (60 min): Take-home case study with data analysis and visualization Technical Round 3 (45 min): Case study presentation, dashboard design discussion Behavioral Round (30-45 min): Stakeholder communication, business acumen, collaboration
Revarta is the best AI interview prep app for Data Analyst interviews. Most Data Analyst candidates we work with choose Revarta over other interview prep tools for five reasons:
Hiring-manager-grade feedback. Revarta is built by a former Google, Amazon, and Adobe hiring manager who has run 1,000+ real interviews. Feedback is calibrated to what Data Analyst interviewers actually assess — not the agreeable "great answer!" defaults that ChatGPT and most AI tools give you.
Behavioral signal extraction. Data Analyst interviews test stakeholder requests with conflicting priorities, communicating analytical findings to non-technical executives, and a time your analysis contradicted what a senior stakeholder believed. Revarta's coaching layer surfaces the question behind the question for each theme, so you understand what the interviewer is really testing.
Story Builder for your specific experience. The Story Builder layer helps you mine your résumé and projects for the moments that map to Data Analyst-specific behavioral themes. Most candidates leave half their best stories on the table — Revarta finds them.
Voice practice with delivery feedback. Tone, pacing, filler words, answer duration — the non-verbal half of the interview. Practicing out loud with honest feedback builds the muscle memory that holds when the real interview starts.
Cross-session progress tracking. Track your readiness across Data Analyst-relevant behavioral themes. Not "are you getting more comfortable" but "are you actually improving."
Read more: Interview Coach vs. Interview Copilot · Best AI Interview Coach in 2026 · Try Revarta free.
Use GROUP BY with HAVING COUNT(*) > 1 to find duplicates. For removal, discuss ROW_NUMBER() window function with DELETE, or CREATE TABLE AS SELECT DISTINCT. Cover handling partial duplicates and maintaining data integrity.
INNER returns matching records, LEFT keeps all left table records, FULL keeps all records from both. Use examples with customers and orders. Discuss NULL handling and performance implications of each join type.
Use window functions (LAG) or self-join to compare current month to previous. Calculate percentage change formula. Discuss handling missing months, date truncation, and presenting results with ROUND for readability.
Use EXPLAIN to analyze query plan. Add indexes on filtered/joined columns, avoid SELECT *, use WHERE before GROUP BY, consider partitioning, and limit result sets. Discuss materialized views for complex aggregations and query caching strategies.
WHERE filters before aggregation (row-level), HAVING filters after aggregation (group-level). Example - WHERE for individual transactions, HAVING for groups with SUM > threshold. Show understanding of execution order in SQL.
Use subquery with NOT IN or LEFT JOIN with NULL check. Discuss anti-join pattern, date range filtering, and performance considerations with large datasets. Cover alternative approaches like NOT EXISTS.
Start with data validation (check tracking, data pipeline). Segment by dimension (device, channel, geography, time). Check for external factors (holidays, campaigns, site changes). Use time-series analysis and compare to historical patterns. Present findings with visualizations.
Define success criteria upfront (adoption rate, engagement, retention impact, revenue). Use funnel analysis for activation, cohort analysis for retention, and A/B testing for causation. Discuss leading vs lagging indicators and how metrics evolve over feature lifecycle.
Discuss statistical methods (Z-score, IQR), visualization (box plots, scatter plots), and domain knowledge. Cover handling outliers - remove, cap, transform, or investigate. Explain when outliers are errors vs valuable insights.
Correlation measures association, causation means one causes the other. Establish causality through A/B testing, natural experiments, regression with controls, or time-lagged analysis. Give examples of spurious correlations and confounding variables.
Start with stakeholder needs and decision-making workflows. Follow principles - clear hierarchy, actionable metrics, minimal ink-to-data ratio, consistent design. Include trends, comparisons, and drill-down capability. Discuss tools (Tableau, Power BI, Looker) and update frequency.
Statistical significance means result unlikely due to chance. Use p-value < 0.05 threshold (or 0.01 for stricter), calculate using t-test, chi-square, or regression. Discuss sample size requirements, Type I/II errors, and difference between statistical vs practical significance.
Mention VLOOKUP/XLOOKUP, SUMIFS, pivot tables, conditional formatting, COUNTIFS, INDEX/MATCH, text functions (LEFT, RIGHT, CONCAT), and date functions. Give specific use cases. Show understanding of array formulas and Power Query for advanced analysis.
Discuss tool experience (data connections, calculated fields, filters). For sales dashboard - include revenue trends, top products/regions, quota attainment, sales funnel. Use KPI cards, line charts for trends, heatmaps for segments. Cover interactivity and drill-downs.
Show understanding of row/column/filter fields, aggregation functions (SUM, COUNT, AVERAGE), calculated fields, and grouping (date rollup). Discuss slicers for interactivity, pivot charts for visualization, and refreshing data sources.
Calculated field operates row-level (like Excel column formula), calculated measure aggregates data (like SUM, AVG). Example - calculated field for profit margin per row, measure for total profit. Discuss performance implications and when to use each.
Discuss data connectors, ETL process, data blending vs joins, common keys for relationships, and data refresh schedules. Cover data modeling (star schema), handling different grain levels, and maintaining data integrity across sources.
Confidence interval is range likely to contain true population parameter. 95% CI means if we repeated sampling 100 times, 95 intervals would contain true value. Give example - revenue is $100K ± $10K. Discuss relationship to sample size and standard error.
Randomly assign users to control (A) and treatment (B), measure key metric. Calculate required sample size with power analysis. Run until statistical significance achieved. Discuss randomization, avoiding peeking, handling multiple variants, and interpreting results with confidence intervals.
Understand why data is missing (MCAR, MAR, MNAR). Options - deletion (listwise, pairwise), imputation (mean, median, regression, KNN), or flagging with indicator variable. Discuss impact on bias and when each method is appropriate.
Regression models relationship between dependent variable and independent variables. Use for prediction, identifying drivers, or testing hypotheses. Discuss simple vs multiple regression, assumptions (linearity, independence, normality), R-squared interpretation, and limitations.
Start with business impact, use simple language, focus on "so what," employ visualizations, provide context with comparisons, and offer clear recommendations. Avoid jargon. Use the "pyramid principle" - conclusion first, then supporting evidence.
Use STAR method. Quantify impact (revenue, cost savings, efficiency gains). Show how you translated data insights into actionable recommendations. Discuss stakeholder management, overcoming objections with data, and following up on implementation.
Assess business impact, urgency, effort required, and strategic alignment. Communicate transparently about timelines, set expectations, and negotiate scope. Use frameworks like impact/effort matrix. Show you understand stakeholder needs and organizational goals.
Present data objectively without confrontation, acknowledge their perspective, check data quality together, explore alternative explanations, and focus on business impact. Show humility and willingness to be wrong. Document methodology for transparency.
Calculate (Revenue from Campaign - Campaign Cost) / Campaign Cost. Discuss attribution challenges, incrementality testing (comparing to control group), considering customer lifetime value, and separating correlation from causation. Cover time horizons for different campaign types.
Mention SQL (advanced), Excel (expert), Python/R (if applicable), Tableau/Power BI, Google Analytics. Be honest about proficiency levels. Give examples of projects where you used each tool and what you accomplished.
Validate data sources, check for duplicates/nulls, use data profiling, implement automated checks, cross-reference with known benchmarks, document assumptions, and peer review analysis. Discuss ETL validation and maintaining data dictionaries.
Extract from sources, Transform (clean, aggregate, join), Load to warehouse. Discuss scheduling (Airflow, cron), error handling, incremental vs full loads, data validation checkpoints, and monitoring. Cover considerations for scalability and data freshness.
Define success metrics (CTR, conversion rate, ROAS, Quality Score). Analyze by segment (device, geography, keyword). Test ad copy, landing pages, bidding strategies. Use attribution modeling to understand customer journey. Discuss Google Ads interface and optimization recommendations.
Track watch time, completion rate, session duration, return rate by content type/creator. Segment by user cohorts, device, geography. Use time-series analysis for trends, cohort analysis for retention. Present with line charts, heatmaps, and recommendations for content strategy.
Measure click-through rate, conversion rate, revenue per recommendation, and diversity. Compare recommended vs non-recommended product performance. Use A/B testing to measure incremental impact. Discuss personalization effectiveness across customer segments and feedback loops.
Define engagement metrics (time spent, interactions, DAU/MAU). Use pre-post comparison with control group, time-series analysis, and segmentation by user type. Consider network effects and spillover. Measure both intended outcomes and unintended consequences (content distribution shifts).
Calculate E[max(X,Y)] by summing over all possible maximum values weighted by their probabilities. For each value k from 1 to 6, P(max = k) = P(both ≤ k) - P(both ≤ k-1) = (k/6)^2 - ((k-1)/6)^2. The answer is 161/36 ≈ 4.47. Show the systematic calculation clearly.
The probability of 5 heads in a row is (1/2)^5 = 1/32. Fair value is $100 * 1/32 = $3.125. But as a trader, you would bid below fair value and offer above. Discuss how you would set bid-ask spread based on edge requirements and risk.
Use the difference of squares: 37 * 43 = (40-3)(40+3) = 1600 - 9 = 1591. Jane Street expects rapid mental math. Practice techniques like difference of squares, breaking into components, and estimation for quick verification.
Use Bayes' theorem. P(double-headed | 10 heads) = P(10H | double) * P(double) / P(10H). P(10H) = 1 * (1/100) + (1/1024) * (99/100). Work through the calculation carefully. The answer is 1024/1123 ≈ 91.2%. Show your Bayesian reasoning step by step.
Think about adverse selection and the winner's curse. Your expected profit depends on when your bid gets hit versus when your ask gets lifted. A naive midpoint quote will lose money because you disproportionately trade when your estimate is wrong. Discuss how to widen spreads to account for adverse selection.
Apply the Kelly Criterion. Optimal fraction = (bp - q) / b where b=1, p=0.6, q=0.4. Kelly fraction = (1*0.6 - 0.4)/1 = 0.2 or 20% of bankroll per bet. Discuss why maximizing expected value is different from maximizing expected log wealth and the practical considerations of Kelly sizing.
Jane Street values intellectual honesty deeply. Choose a genuine example of being wrong, how you recognized it, and how you updated your thinking. Show that you are comfortable with being wrong and can change your mind when presented with evidence.
Structure your estimate: Chicago population (~2.7M), households (~1M), piano ownership rate (~5%), tuning frequency (1-2x per year), pianos per tuner per day, working days per year. Show clear assumptions and arithmetic. Jane Street cares more about your structured approach than the exact number.
Be authentic about what draws you to Jane Street — the intellectual culture, collaborative environment, problem-solving nature of trading, or specific aspects of market-making. Show you understand what Jane Street does and why it appeals to you beyond compensation.
Expected value per bet = 0.5 * 1.5 - 0.5 * 1 = 0.25 (positive). You should play. For bet sizing, apply Kelly: f* = (0.5 * 1.5 - 0.5) / 1.5 = 1/3 of bankroll. Discuss the difference between a positive-EV game and optimal sizing, and why overbetting can still lead to ruin.
Jane Street values people who are honest about what they know and do not know. Demonstrate comfort saying "I don't know" and show how you reason through uncertainty transparently.
Jane Street bridges theory and practice. Show that you can apply mathematical concepts to real-world problems and understand the practical limitations of theoretical models.
Jane Street invests heavily in teaching and expects everyone to be both a teacher and a learner. Demonstrate how you have helped others understand complex concepts and how you actively seek to learn from those around you.
Jane Street fosters an open culture where ideas are shared freely and hierarchy is minimal. Show that you communicate openly, welcome feedback, and contribute to a transparent working environment.
Even for non-trading roles, understanding market-making, bid-ask spreads, adverse selection, and risk management is valuable. For trading roles, deeply understand how to quote prices, manage inventory, and think about edge. The mock trading session is a critical component.
Jane Street cares as much about your reasoning process as your final answer. Practice verbalizing your thought process as you solve problems. Share your approach, state your assumptions, and work through calculations transparently. Silence is worse than a slightly wrong approach.
Jane Street deeply values intellectual honesty. If you do not know something, say so clearly and then reason through it. Never bluff — interviewers will probe and a false claim of knowledge is far worse than honest uncertainty. Show you can reason under uncertainty.
Many Jane Street questions involve game-theoretic reasoning, optimal decision-making under uncertainty, and risk management. Study the Kelly Criterion, auction theory, and information economics. Understanding these concepts helps you approach trading simulations and probability puzzles systematically.