Swiggy Business 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

   During a Genie experiment, the treatment/control split is 52/48 instead of 50/50. How would you diagnose sample ratio mismatch?

   MediumStatistics & Experimentation RoundA/B TestingSwiggy-specific

   Context: Assume assignment logs, exposure logs, and eligibility filters may disagree.

   How to answer: To diagnose a 52/48 split instead of 50/50 in a Genie A/B test, I would first check the randomization logic and implementation for any biases, such as user ID hashing issues or sequential assignment. Next, I'd analyze the traffic distribution over time, looking for sudden shifts or specific user segments disproportionately assigned to one group. I would also investigate any pre-experiment filters, targeting rules, or concurrent experiments that might interfere with the sample assignment. Finally, I'd verify the data logging and aggregation process to ensure accurate counting of users in each group.

2. Q2

   A new Delivery Partner App feature shows a large week-1 lift in order conversion rate, but the effect fades by week 4. What could explain this and how would you design the test duration?

   MediumStatistics & Experimentation RoundA/B TestingSwiggy-specific

   Context: Discuss novelty, learning effects, seasonality, and durable impact.

   How to answer: The fading effect suggests a novelty effect or a change in user behavior that isn't sustained. Possible explanations include initial curiosity driving engagement, a learning curve that eventually normalizes behavior, or a temporary incentive that expired. To design the test duration, one should consider the typical user habit formation period for delivery partners, the lifecycle of similar features, and potential seasonality. A minimum duration of 4-6 weeks is often recommended to capture stabilization, with an eye towards longer periods (8-12 weeks) if habit formation or long-term behavioral shifts are critical to the feature's success.

3. Q3

   Treatment improves order conversion rate but worsens delivery SLA breach rate for Restaurant Ads. Walk through a launch recommendation

   HardStatistics & Experimentation RoundA/B TestingSwiggy-specific

   Context: Make a decision under conflicting metrics and quantify tradeoffs for stakeholders.

   How to answer: A strong recommendation would involve quantifying the trade-off between improved conversion and worsened SLA breach. This requires calculating the monetary impact of both metrics, considering customer lifetime value, churn risk, and potential refunds/penalties from SLA breaches. The recommendation should then propose a phased rollout or targeted launch, perhaps to specific restaurant types or customer segments, while continuously monitoring key metrics. Finally, it should suggest further investigation into the root cause of the SLA breach and potential mitigation strategies before a full-scale launch.

4. Q4

   Midway through the Food Delivery test, tracking for Swiggy One changed. How would you decide whether the experiment results are still usable?

   HardStatistics & Experimentation RoundA/B TestingSwiggy-specific

   Context: Compare instrumentation versions, affected traffic share, raw logs, and sensitivity analyses.

   How to answer: First, identify the exact nature and timing of the tracking change for Swiggy One relative to the Food Delivery test's start and end. Then, assess if the Swiggy One tracking change directly impacts the primary or secondary metrics of the Food Delivery test, or if Swiggy One users are a significant segment of the Food Delivery test population. Analyze pre- and post-change data for both control and experiment groups to detect any sudden shifts or discrepancies in key metrics, especially for Swiggy One users. Finally, determine if the observed impact is significant enough to invalidate the experiment's core assumptions or introduce bias, potentially requiring a restart or a segmented analysis.

5. Q5

   Swiggy is considering launching Swiggy One in a new city. Build a decision framework and the first 90-day success metrics

   MediumProduct Analytics & Business CaseBusiness CasesSwiggy-specific

   Context: Include demand, supply, operations, compliance, cost, and competitive positioning.

   How to answer: A strong answer will first define a decision framework for launching Swiggy One, likely involving market potential (TAM, competition, existing Swiggy penetration), operational feasibility (delivery network, restaurant density), and financial viability (CAC, LTV, break-even). This framework should include criteria and a scoring mechanism. For the first 90-day success metrics, the candidate should focus on early indicators of adoption and operational efficiency, such as Swiggy One subscription sign-ups, active subscriber rate, order frequency per subscriber, delivery time for One orders, and restaurant partner participation in One benefits. Qualitative feedback from early adopters and delivery partners would also be a good addition.

6. Q6

   Refunds, cancellations, or failures are rising for Genie. Quantify the business impact and recommend where to intervene first

   HardProduct Analytics & Business CaseBusiness CasesSwiggy-specific

   Context: Break the problem into customer experience, partner quality, operations, and policy effects.

   How to answer: A strong candidate would first quantify the business impact by calculating the total monetary loss due to refunds, cancellations, and failures, considering both direct costs (e.g., refund amount, delivery partner payout for failed orders) and indirect costs (e.g., customer churn, brand reputation). They would then break down the problem by identifying the root causes for each category (refunds, cancellations, failures) and segmenting by factors like time of day, location, item type, or delivery partner. Finally, they would recommend intervention points based on the highest impact and feasibility, prioritizing areas with significant monetary loss or high frequency, and suggest specific data-driven solutions like process improvements, technology enhancements, or training.

7. Q7

   Fraud, abuse, or policy gaming is suspected in Food Delivery. Size the financial impact and propose an analytics approach to reduce it

   HardProduct Analytics & Business CaseBusiness CasesSwiggy-specific

   Context: Balance loss prevention with false positives and user/partner experience.

   How to answer: A strong candidate would first define common fraud/abuse scenarios in food delivery (e.g., fake orders, promo abuse, driver collusion). They would then propose a sizing methodology, including data sources (transaction logs, refund data, promo redemptions) and estimation techniques (sampling, rule-based flagging, historical trends). For the analytics approach, they would suggest a multi-pronged strategy: anomaly detection (ML models), rule-based systems, network analysis to identify colluding entities, and A/B testing for policy changes. Finally, they would touch upon monitoring, feedback loops, and the importance of balancing fraud reduction with customer experience.

8. Q8

   Instamart is preparing for a high-traffic event or sale. What metrics and analyses would you use to prevent a business-critical failure?

   HardProduct Analytics & Business CaseBusiness CasesSwiggy-specific

   Context: Think capacity, inventory, payments, support load, latency, and real-time alerting.

   How to answer: A strong candidate would outline a pre-event strategy focusing on demand forecasting, capacity planning, and system readiness. Key metrics include historical sales data, promotional uplift factors, and competitor activity for demand prediction. For capacity, they'd consider rider availability, dark store inventory levels, and last-mile logistics capacity. System readiness involves monitoring API latency, database performance, and payment gateway success rates through load testing and real-time dashboards.

9. Q9

   Design a funnel dashboard for Restaurant Ads from first exposure to delivered order. How would you highlight the biggest conversion opportunities?

   MediumDashboarding, Stakeholder & Hiring Manager RoundDashboardingSwiggy-specific

   Context: Include step-level conversion, drop-off contribution, trend, and segmentation.

   How to answer: A strong answer would outline a multi-stage funnel, starting from Ad Impression, through Ad Click, Restaurant Page View, Menu Item View, Add to Cart, Checkout Initiated, Order Placed, and finally, Order Delivered. Key metrics for each stage, such as conversion rates, drop-off rates, and absolute numbers, should be identified. To highlight conversion opportunities, the candidate should propose using visual cues like large drop-off percentages, 'red-flag' indicators, or comparative analysis (e.g., against benchmarks or A/B test results) at each stage. They should also suggest segmenting the data by ad campaign, restaurant type, user segment, or time to pinpoint specific areas for improvement.

10. Q10

    A Food Delivery dashboard is slow and users export raw data instead. How would you improve performance and adoption?

    MediumDashboarding, Stakeholder & Hiring Manager RoundDashboardingSwiggy-specific

    Context: Discuss aggregated tables, filters, caching, chart pruning, and stakeholder training.

    How to answer: To improve dashboard performance, I would first investigate the root cause of slowness, focusing on data source optimization (e.g., pre-aggregation, indexing, materialized views) and dashboard design efficiency (e.g., reducing widgets, optimizing calculations, using filters effectively). For adoption, I'd conduct user interviews to understand pain points and desired features, then redesign the dashboard for intuitive navigation, clear visualizations, and actionable insights. Finally, I would implement user training, create documentation, and establish a feedback loop to ensure continuous improvement and drive engagement.

11. Q11

    How would you audit a dashboard for Instamart after stakeholders report that numbers do not match finance or operations reports?

    MediumDashboarding, Stakeholder & Hiring Manager RoundDashboardingSwiggy-specific

    Context: Trace metric definitions, source tables, filters, timezones, freshness, and access rules.

    How to answer: A strong candidate would first define the scope by identifying the specific metrics and reports that are mismatched. They would then systematically verify the data sources, ensuring all reports pull from the correct, primary systems (e.g., order management, finance ledger). The next step involves validating the data transformation logic, checking SQL queries, aggregation rules, and any business logic applied within the dashboard against the finance/operations report's methodology. Finally, they would compare the data at different granularities (daily, weekly, monthly) and specific dimensions (store, item category) to pinpoint discrepancies, document findings, and propose corrective actions.

12. Q12

    Design a retention analysis for Swiggy One. Which cohorts, time windows, and segments would you use?

    EasyProduct Analytics & Business CaseProduct AnalyticsSwiggy-specific

    Context: Make the cohort definition precise and explain how you would separate activation from retention.

    How to answer: To design a retention analysis for Swiggy One, I would primarily use subscription start date as the cohort definition, grouping users by the month they first subscribed. Time windows would be measured in weeks or months post-subscription, tracking active usage of Swiggy One benefits (e.g., free delivery, exclusive discounts). Key segments for comparison would include users acquired through different channels (e.g., in-app promo, partner offer), users with varying initial engagement levels (e.g., number of orders in first week), and users who received specific onboarding flows. This would allow us to identify which acquisition methods or initial experiences lead to higher long-term retention.

13. Q13

    How would you segment customers for Restaurant Ads to find growth opportunities? Name segments, metrics, and potential actions

    MediumProduct Analytics & Business CaseProduct AnalyticsSwiggy-specific

    Context: Use behavioral, value, lifecycle, and acquisition dimensions.

    How to answer: To segment customers for Restaurant Ads at Swiggy, I would start by categorizing them based on their ad interaction history (e.g., ad clickers vs. non-clickers), order frequency (new, occasional, frequent), and cuisine preferences. Key metrics for each segment would include Ad Click-Through Rate (CTR), Conversion Rate (Ad-to-Order), Average Order Value (AOV) from ad-driven orders, and incremental revenue. Potential actions would involve A/B testing different ad creatives and offers for new users, retargeting lapsed ad clickers with personalized deals, and upselling frequent users with premium restaurant ads or exclusive launches.

14. Q14

    Create an operational scorecard for Restaurant Ads using delivery SLA breach rate. Which leading and lagging indicators would you include?

    MediumProduct Analytics & Business CaseProduct AnalyticsSwiggy-specific

    Context: Make it useful for daily operations, not just monthly reporting.

    How to answer: A strong answer would define the operational scorecard's purpose for Restaurant Ads, focusing on delivery SLA breach rate. It would clearly differentiate between leading indicators like 'Ad-induced order volume spike' and 'Restaurant staff availability' (for ads-driven orders) and lagging indicators such as 'Actual delivery SLA breach rate for Ad orders' and 'Restaurant churn rate from Ads program due to poor performance'. The candidate should also suggest key metrics for each indicator and how they relate to improving the ad product's reliability and restaurant satisfaction.

15. Q15

    For Restaurant Ads, write SQL to calculate the median time from restaurant_view to order_placed using order_events. What changes if the SQL dialect lacks a built-in percentile function?

    EasySQL Technical ScreenSQLSwiggy-specific

    Context: Hypothetical Swiggy analytics case for Restaurant Ads; schemas may be simplified for an interview. The interviewer may ask for both a concise version and a portable version.

    How to answer: The candidate should identify the need to join `order_events` to itself to find pairs of `restaurant_view` and `order_placed` for the same `session_id` and `user_id`. They then need to calculate the time difference between these two events. The core of the answer involves using a window function like `NTILE(2)` or `ROW_NUMBER()` to determine the median, or directly using a `PERCENTILE_CONT(0.5)` function if available. If percentile functions are not available, the candidate should describe how to calculate the median manually by ordering the time differences and selecting the middle value(s) based on whether the count is odd or even.

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Prepare coding/SQL and analytical cases; show product/data sense; quantify impact

Indicative Business Analyst pay in India: ~₹7–26 LPA (role-level range, not a Swiggy-specific figure).

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