Swiggy Data Analyst Interview Questions (2026)

1. 1Problem Solving Screen45 minMedium

   One or two medium DSA problems in a shared editor, judged on working code and complexity analysis.

2. 2Problem Solving / DSA Round 255 minHard

   Harder algorithmic round covering trees, graphs, or DP, often with a follow-up that adds a real-world constraint.

3. 3Design Round (LLD/HLD)60 minHard

   LLD machine-coding of a module (e.g. a splitwise-style ledger or rate limiter) for SDE-1/2, or HLD of a delivery-scale system for SDE-3 and above.

4. 4Data / Analytics Round50 minMedium

   SQL on order and delivery datasets plus a metrics case such as diagnosing a drop in Instamart conversion or designing an experiment.

5. 5Hiring Manager Round50 minMedium

   Deep project walkthrough plus situational behavioral questions on ownership, customer focus, and handling conflicting priorities.

6. 6HR Round30 minEasy

   Offer discussion covering compensation structure, ESOPs, notice period, and team allocation.

1. Q1

   Choose primary and guardrail metrics for a Swiggy One experiment aimed at improving order conversion rate. What metrics would prevent a harmful launch?

   MediumStatistics & Experimentation RoundA/B TestingSwiggy-specific

   Context: Include user experience, partner health, revenue, reliability, and long-term retention considerations.

   How to answer: For a Swiggy One experiment targeting order conversion, the primary metric would be 'Order Conversion Rate' (orders / sessions or unique users reaching checkout). Guardrail metrics are crucial to prevent negative side effects. Key guardrails would include 'Average Order Value (AOV)', 'Customer Retention Rate' (especially for Swiggy One subscribers), 'Delivery Partner Utilization/Earnings', and 'Customer Support Contact Rate' related to experiment features. These guardrails ensure the conversion uplift isn't achieved at the expense of revenue, customer loyalty, operational efficiency, or user experience.

2. Q2

   The Restaurant Ads experiment is trending positive after two days. A PM wants to stop early and launch. How do you handle peeking and sequential testing?

   MediumStatistics & Experimentation RoundA/B TestingSwiggy-specific

   Context: Discuss pre-specified stopping rules, alpha spending, business urgency, and risk.

   How to answer: Explain that peeking early in an A/B test increases the Type I error rate (false positive) due to multiple comparisons. Discuss the need for a pre-determined sample size and test duration based on power analysis to ensure statistical validity. Propose methods like Bonferroni correction or O'Brien-Fleming boundaries for sequential testing if early stopping is truly necessary and planned. Emphasize communicating the risks of early stopping to the PM and advocating for completing the planned experiment duration.

3. Q3

   In a marketplace-like Food Delivery feature, treatment users may affect control users. How would network effects or interference bias the experiment?

   MediumStatistics & Experimentation RoundA/B TestingSwiggy-specific

   Context: Examples include restaurant supply, content inventory, delivery capacity, or pricing pressure.

   How to answer: Network effects in a food delivery marketplace can lead to interference, where the treatment group's actions impact the control group, biasing A/B test results. This typically manifests as 'spillover effects,' where, for example, increased demand from treatment users might reduce driver availability or increase delivery times for control users, making the control group perform worse than it would in isolation. Conversely, if the treatment reduces demand, control users might see improved service. This interference violates the Stable Unit Treatment Value Assumption (SUTVA), making it difficult to accurately measure the true causal effect of the treatment.

4. Q4

   order conversion rate is a low-frequency event for Instamart. How would you set up an experiment with enough power without waiting too long?

   MediumStatistics & Experimentation RoundA/B TestingSwiggy-specific

   Context: Discuss proxy metrics, variance reduction, larger samples, longer windows, and risk of metric gaming.

   How to answer: To set up an A/B test for a low-frequency event like Instamart order conversion without excessive wait times, focus on selecting a suitable primary metric and potentially leveraging proxy metrics. The primary metric should ideally be a higher-frequency event that correlates strongly with order conversion, such as 'add-to-cart rate' or 'time spent browsing Instamart'. Additionally, consider increasing the sample size significantly by expanding the user base exposed to the experiment, and utilize sequential testing methodologies to allow for early stopping if a clear winner emerges, or if the experiment is unlikely to reach significance.

5. Q5

   Design a geo or city-level experiment for Swiggy One. When is this better than user-level randomization, and what are the analytical downsides?

   MediumStatistics & Experimentation RoundA/B TestingSwiggy-specific

   Context: Use matched markets, pre-period balancing, spillover checks, and fewer experimental units.

   How to answer: A strong candidate would outline an experiment design for Swiggy One using geo-level randomization, specifying control and treatment cities/geos, and key metrics like subscription rate, order frequency, and GMV. They would explain that geo-level randomization is superior when there's a risk of network effects (e.g., word-of-mouth, shared accounts) or when the intervention itself is inherently geo-specific (e.g., marketing campaigns, delivery logistics changes). The analytical downsides include lower statistical power due to fewer experimental units, increased risk of selection bias if geo-level units aren't well-matched, and potential for spillover effects between adjacent geos.

6. Q6

   How would you design ramp-up, holdback, and post-launch monitoring for a successful Delivery Partner App A/B test?

   HardStatistics & Experimentation RoundA/B TestingSwiggy-specific

   Context: Include ramp stages, persistent holdback, alert thresholds, rollback criteria, and owner accountability.

   How to answer: For ramp-up, I'd start with a small percentage (e.g., 1-5%) of the target Delivery Partner population, monitoring key operational metrics like app crashes, login failures, and order acceptance rates for stability before gradually increasing exposure. Holdback involves reserving a small, unexposed control group (e.g., 1-2%) for an extended period post-launch to detect long-term novelty effects or sustained behavioral changes. Post-launch monitoring requires continuous tracking of primary metrics (e.g., delivery time, partner earnings, cancellations) and guardrail metrics (e.g., app stability, support contacts) via dashboards with clear alerts, coupled with periodic re-evaluation of the holdback group's performance against the fully launched experience.

7. Q7

   Two overlapping experiments on Instamart both affect contribution margin per order. How would you detect and manage interaction effects?

   HardStatistics & Experimentation RoundA/B TestingSwiggy-specific

   Context: Discuss experiment registry, factorial design, exclusion rules, and interaction terms.

   How to answer: A strong candidate would first emphasize pre-experiment design to minimize overlap, but acknowledge it's not always possible. They would then propose statistical methods like ANCOVA or regression analysis with interaction terms to detect significant interaction effects between the two experiments. Managing these effects involves either sequential rollout, re-designing one or both experiments, or interpreting results with the interaction effect quantified. Finally, they would discuss the importance of clear documentation and communication regarding overlapping experiments and their potential interactions.

8. Q8

   Swiggy wants to launch or expand an ads/merchant monetization product related to Restaurant Ads. What business metrics decide whether it is worth scaling?

   HardProduct Analytics & Business CaseBusiness CasesSwiggy-specific

   Context: Balance advertiser/partner value, customer experience, organic conversion, and incremental profit.

   How to answer: To decide if scaling a Restaurant Ads product is worthwhile, Swiggy should primarily focus on revenue metrics like Average Revenue Per User (ARPU) for advertisers, Gross Merchandise Value (GMV) uplift attributed to ads, and Return on Ad Spend (ROAS) for merchants. Concurrently, evaluate advertiser engagement through metrics such as active advertisers, ad impression share, and click-through rates (CTR). Finally, monitor user experience metrics like order conversion rates and customer satisfaction to ensure ads don't detract from the core platform experience, balancing monetization with user retention.

9. Q9

   You need to create a self-serve dashboard for Swiggy One that PMs and business teams will use weekly. How do you define metrics and prevent misuse?

   EasyDashboarding, Stakeholder & Hiring Manager RoundDashboardingSwiggy-specific

   Context: Include metric glossary, data freshness, filters, caveats, and examples.

   How to answer: To define metrics for a Swiggy One self-serve dashboard, begin by understanding the core business objectives (e.g., subscriber growth, retention, order frequency, GMV per subscriber). Translate these into actionable KPIs, categorizing them into acquisition, engagement, and retention metrics, ensuring clear definitions and calculation methodologies. To prevent misuse, implement robust data governance, provide comprehensive documentation for each metric, and offer training sessions. Additionally, design the dashboard with intuitive visualizations, clear filters, and contextual information to guide users.

10. Q10

    Build a cohort dashboard for Genie. Which cohort definitions, retention views, and segment controls should it have?

    MediumDashboarding, Stakeholder & Hiring Manager RoundDashboardingSwiggy-specific

    Context: Prioritize clarity over chart count and make denominator definitions visible.

    How to answer: A strong answer will define cohorts based on user acquisition month (first Genie order) and potentially first order type (e.g., specific category like 'pick-up and drop'). Retention views should include N-day retention (e.g., 7-day, 30-day) showing the percentage of users from a cohort who placed at least one subsequent Genie order within that timeframe, and potentially order frequency/value. Segment controls are crucial, allowing filtering by city, user type (new vs. existing Swiggy user), order value bands, and service type (e.g., 'send' vs. 'receive' items) to identify specific cohort behaviors and opportunities.

11. Q11

    Instamart's conversion from restaurant_view to order_placed dropped 15% week over week. Walk through your diagnosis plan

    EasyProduct Analytics & Business CaseProduct AnalyticsSwiggy-specific

    Context: Assume no single obvious outage has been announced.

    How to answer: A strong diagnosis plan starts by clarifying the scope (Instamart vs. overall Swiggy, specific week) and confirming the data's accuracy. Then, segment the conversion drop by key dimensions like city, user segment (new vs. existing), device type, and time of day to pinpoint specific areas. Next, analyze upstream and downstream metrics to identify if the issue is with traffic quality (restaurant_view) or checkout funnel friction (order_placed). Finally, investigate recent changes (A/B tests, deployments, marketing campaigns, competitor actions) that could explain the drop.

12. Q12

    A new Genie feature has 30% adoption but no movement in order conversion rate. What analyses would you run before calling it unsuccessful?

    EasyProduct Analytics & Business CaseProduct AnalyticsSwiggy-specific

    Context: Consider exposure, eligibility, frequency, quality of adoption, and segment fit.

    How to answer: First, I'd segment users by adoption status (adopters vs. non-adopters) to compare their conversion rates and identify any positive impact within the adopting group. Next, I would analyze feature usage patterns among adopters to understand engagement depth and identify potential friction points. I'd also investigate if the feature targets a specific user segment or use case where conversion uplift might be expected, and check for any negative impacts on other metrics or user groups. Finally, I would consider the feature's intended goal beyond immediate order conversion, such as retention or average order value, and analyze those metrics.

13. Q13

    Evaluate search or discovery quality for Instamart. Which metrics would tell you whether users are finding what they want?

    MediumProduct Analytics & Business CaseProduct AnalyticsSwiggy-specific

    Context: Include query refinement, zero results, click depth, conversion, and long-term satisfaction.

    How to answer: A strong answer would define search/discovery quality as the user's ability to efficiently find desired items. Key metrics include search-to-add-to-cart rate, search conversion rate (search to order), and the ratio of 'no results' searches. Additionally, analyzing search query reformulation rate, click-through rate on search results, and time spent on search results pages can indicate friction. For discovery, metrics like category browse-to-add-to-cart, unique items viewed per session, and repeat purchase rate for discovered items are crucial. Qualitative feedback and A/B testing different search algorithms or UI elements would also be mentioned.

14. Q14

    Monetization increased for Genie, but order conversion rate and satisfaction declined. How would you frame the tradeoff?

    MediumProduct Analytics & Business CaseProduct AnalyticsSwiggy-specific

    Context: Quantify incremental revenue, user harm, partner impact, and reversibility.

    How to answer: The tradeoff is between short-term revenue gain (monetization) and long-term user engagement/retention (conversion, satisfaction). Increased monetization likely introduced friction or cost, impacting user experience. A strong answer would identify potential monetization strategies (e.g., higher delivery fees, surge pricing, mandatory tips) and their direct impact on conversion and satisfaction. The key is to analyze the elasticity of demand for Genie services and the long-term value of a satisfied, repeat customer versus immediate revenue per order. Ultimately, the goal is to find an optimal balance that maximizes lifetime value.

15. Q15

    Estimate customer lifetime value for Delivery Partner App. What inputs would you use, and how would you handle early-tenure customers?

    MediumProduct Analytics & Business CaseProduct AnalyticsSwiggy-specific

    Context: Discuss gross margin, retention, frequency, discounting, cohort maturity, and uncertainty.

    How to answer: To estimate CLTV for Delivery Partners, I would use inputs like average order value (AOV) per delivery, average number of deliveries per partner per month, partner retention rate, and Swiggy's gross margin on delivery fees. The calculation would involve multiplying AOV by average deliveries per month, then by gross margin, and then by a factor derived from the retention rate (e.g., 1 / (1 - retention rate) for a simplified model). For early-tenure customers (new delivery partners), I would segment them and apply a separate, potentially lower, retention rate and average activity level, or use a cohort-based analysis to observe their initial ramp-up and churn more accurately, as their behavior is not yet stabilized.

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