agoda Data Analyst Interview: Questions, Experience & Prep (2026)

Agoda, the travel tech company under Booking Holdings, is one of the most active Data Analyst hirers in India right now. The knok jobradar (as of July 2026) shows 299 open Data Analyst roles at Agoda, out of 319 total Data Analyst openings tracked across India. Bangalore leads city-wise with 41 openings, followed by Delhi (22) and Mumbai (19).

The interview process at Agoda typically runs three to five rounds: an initial HR call, one or two technical rounds focused on SQL and analytics thinking, a take-home case study or business problem, and a final round with a senior stakeholder or hiring manager. Candidates report that rounds are practical and product-focused rather than theoretical. Agoda looks for analysts who can translate data into decisions that move a travel business metric, not just people who can write queries.

Salary bands for Data Analysts in India, based on the knok jobradar data:

These bands reflect broad market data. Agoda-specific offers may vary by role scope and city.

Candidates who have gone through Agoda's Data Analyst interviews (as reported on community forums) commonly see these types of questions. Treat this as a preparation checklist, not a guaranteed question bank.

1. Write a SQL query to find the top 5 cities by hotel booking revenue for the last quarter, excluding cancelled bookings.
2. A key conversion metric drops suddenly. Walk me through how you would investigate this.
3. How would you design an A/B test to measure the impact of a new search ranking algorithm on booking rates?
4. You have two datasets: one from the app team and one from the backend. They show different user counts for the same date. How do you reconcile them?
5. Describe a time you used data to change a business decision that was already in motion.
6. How would you measure the success of a newly launched hotel loyalty programme?
7. A product manager asks you for a dashboard. What questions do you ask before building it?
8. Given a table of user sessions and a table of bookings, write a query to calculate the booking conversion rate per traffic source.
9. How would you detect and handle outliers in booking price data?
10. Agoda operates across many countries with different currencies and seasonal patterns. How would you build a forecasting model that accounts for this?
11. A senior stakeholder disagrees with your analysis. How do you handle it?
12. Walk me through a window function or self-join you have used in a real project.

Use the STAR format (Situation, Task, Action, Result) for all behavioural questions. Below are three worked examples tailored to Agoda's travel-tech context.

Q: Tell me about a time you discovered a major data discrepancy and what you did.

*Situation:* At my previous company, our weekly revenue report showed a sudden spike that did not match the finance team's numbers.
*Task:* I needed to identify the root cause quickly so the leadership team could trust the next morning's business review.
*Action:* I traced the ETL pipeline step by step, compared raw source data against the aggregated table, and found that a currency conversion job had applied the wrong exchange rate for a brief window. I documented the exact rows affected and the corrected figures.
*Result:* The finance team was able to restate the numbers before the business review. I also added a daily reconciliation check to the pipeline so the same error would surface automatically in future.

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Q: Describe a time you influenced a product decision using data.

*Situation:* Our mobile app team planned to remove a filter feature from the search page, believing it was rarely used.
*Task:* I was asked to validate or challenge that assumption before the feature was taken off the roadmap.
*Action:* I segmented usage by user cohort and found that while the overall click rate was low, users who engaged with the filter converted at a much higher rate and had a higher average order value, according to our internal analytics. I built a short presentation showing the revenue at risk and presented it to the product manager with a recommendation to retain the filter for high-intent users.
*Result:* The team decided to keep the filter and run an A/B test instead of removing it outright. The test confirmed the cohort insight, and the feature was retained.

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Q: Tell me about a time you had to work with messy or incomplete data.

*Situation:* I was building a churn prediction model for a subscription product, but the historical cancellation data had gaps spanning several months due to a logging bug.
*Task:* I needed to decide whether to exclude the affected period or find a way to impute it without introducing bias.
*Action:* I documented the gap clearly, cross-referenced server logs and payment records to partially reconstruct missing cancellations, and flagged the imputed rows with a confidence score. I then ran the model both with and without the imputed data to check stability.
*Result:* The two model versions produced similar outputs, which gave the team confidence to proceed. I included a data-quality caveat in the final report so stakeholders understood the limitation.

For SQL and technical questions: Think out loud from the start. State your assumptions (date range, join type, how you handle NULLs) before writing the query. Agoda's data likely involves bookings, users, properties, and sessions, so practise joins across these entity types. Window functions (RANK, LAG, LEAD, running totals) come up often in travel analytics.

For metric deep-dives (the 'metric dropped' question): Use a structured breakdown. Start by confirming the drop is real (data pipeline issue?), then segment by dimension (device, country, traffic source, property type) to isolate where the drop is concentrated. Candidates report that Agoda interviewers reward candidates who ask clarifying questions before jumping to conclusions.

For A/B testing questions: Cover the full lifecycle: hypothesis, randomisation unit (user vs. session vs. property), sample size reasoning (reference industry-standard power calculations rather than quoting a specific number), metric choice (primary and guardrail), duration, and how you would decide to ship or not ship.

For business case questions: Lead with the metric you would use to define success, then describe how you would measure it, what data sources you need, and what a positive vs. negative result looks like. Tie your answer back to a travel business outcome (bookings, revenue, repeat rate) rather than a generic analytics outcome.

For behavioural questions: Use STAR every time. Keep the Situation brief (one or two sentences), spend most of your time on Action, and make the Result concrete even if you cannot quote a specific number (for example: 'the team adopted the recommendation' or 'the model went into production').

Candidates report that Agoda interviewers consistently look for a few things beyond raw technical skill.

Business curiosity over pure technical fluency. Agoda is a product company at heart. Interviewers want to see that you ask 'why does this metric matter?' before you ask 'how do I calculate it?'. An analyst who can write perfect SQL but cannot connect it to a booking funnel outcome is less valuable to them than someone who does both.

Comfort with ambiguity. Travel data is messy: seasonality, currency differences, multi-market behaviour, and irregular booking patterns all create noise. Interviewers will often leave questions deliberately vague to see if you ask clarifying questions or just charge ahead with assumptions.

Clear communication. Many Data Analyst roles at Agoda involve working closely with product managers and country managers who are not technical. Candidates report that being asked to 'explain this to a non-data person' is common. Practise translating your analysis into a business implication in one or two plain sentences.

Ownership mindset. Agoda operates at scale across many markets. They want analysts who follow through: not just flagging a problem but proposing a fix, not just building a dashboard but checking if anyone uses it. In your answers, show what happened after the analysis, not just the analysis itself.

Structured thinking under pressure. The case study and SQL rounds are often time-limited. Interviewers are watching whether you can organise your thoughts quickly and communicate a logical approach even if you do not reach a perfect final answer.

Week 1: SQL and data foundations
Practise intermediate to advanced SQL daily. Focus on window functions, subqueries, CTEs, and multi-table joins. Use datasets that resemble travel data (orders, sessions, products, dates) to make practice feel relevant. Candidates report that Agoda's SQL questions are practical and business-contextual rather than algorithmic puzzles.

Week 2: Analytics frameworks and metric design
Pick three or four product metrics (conversion rate, retention, average booking value) and practise breaking them down by dimension and diagnosing drops. Practise designing an A/B test end to end, including explaining the randomisation unit and success criteria without quoting specific numbers you cannot back up.

Week 3: Behavioural stories and case prep
Write out five to six STAR stories from your own experience covering: a data discrepancy you caught, a time you influenced a decision, a time you worked with messy data, a time you disagreed with a stakeholder, and a time you delivered under pressure. Refine each story so the Action and Result are crisp and specific.

Week 4: Mock rounds and Agoda context
Do at least two timed mock interviews with a peer or in front of a camera. Read Agoda's publicly available content about their markets and products so you can use relevant examples in case questions. Review your SQL under time pressure: can you write a clean query in a few minutes without an IDE?

Ongoing: Track your job applications. Knok checks 150+ job sites nightly, applies to jobs matching your resume, and messages HR for you, so you can focus your energy on interview prep rather than application logistics.

Jumping into SQL without clarifying the question. Candidates often start typing before confirming scope: which date range, how to treat NULLs, whether 'revenue' means gross or net. A few clarifying questions take a short time and save you from solving the wrong problem.

Giving generic answers to business questions. Saying 'I would segment the data' is not an answer. Agoda interviewers want to know which segments, why those segments, and what you would do with the result. Be specific about the travel context.

Ignoring data quality in case studies. Many candidates build an analysis assuming the data is clean. Agoda deals with real-world travel data across dozens of markets. Mentioning data validation, pipeline checks, or outlier handling signals maturity.

Over-engineering the technical solution. If the question can be answered with a straightforward GROUP BY and a WHERE clause, write that. Do not add unnecessary complexity to impress the interviewer. Readability matters in a team setting.

Weak behavioural answers. Vague stories ('I improved the dashboard and the team liked it') do not land. Every STAR story needs a concrete result, even a qualitative one such as 'the product team reversed the decision' or 'the model went live and is still in use'.

Not asking questions at the end. Agoda interviewers report that strong candidates ask thoughtful questions about the team's data stack, the biggest analytics challenges in the role, or how success is measured in the first few months. Having no questions signals low interest.