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

Atlys is a visa and travel-tech company that helps people apply for visas online, cutting through paperwork and long queues. As a Data Analyst at Atlys, you would typically work on product funnel metrics, user behaviour analysis, visa approval patterns, and operational dashboards that help the business move faster.

As of July 2026, Atlys has 29 open roles tracked on knok jobradar, which signals active hiring across functions including data. The interview process candidates report usually involves a recruiter screening call, a technical round (SQL or take-home analysis), and one or two rounds with a hiring manager or cross-functional stakeholder. Because Atlys is product-led, interviews blend SQL proficiency with product thinking and the ability to turn data into a clear business story.

Salary bands for Data Analyst roles in India tracked on knok jobradar range from 5-10 LPA at entry level (0-2 years) to 10-18 LPA at mid-level (3-5 years) and 18-30 LPA at senior level (6-9 years). Lead-level roles go up to 28-45+ LPA.

Based on what candidates report and the nature of Atlys's product, these questions come up most often.

1. Walk me through how you would design a dashboard to track visa application conversion rates at each step of the funnel.
2. Write a SQL query to identify users who started a visa application but did not complete it within a set time window.
3. How would you define and measure the success of a new feature Atlys just launched?
4. Atlys processes applications across many countries and visa types. How would you segment users to find which groups are dropping off?
5. You notice a sudden drop in completed visa applications on a particular day. How do you investigate it?
6. How would you approach predicting which users are likely to abandon their application before finishing it?
7. Tell me about a time you presented a data finding that changed a business decision.
8. How do you handle missing or inconsistent data in a dataset that comes from multiple countries and sources?
9. What metrics would you track to measure the health of Atlys's user acquisition funnel?
10. How would you design an A/B test to evaluate a new onboarding flow for first-time applicants?
11. Describe a time you worked closely with a product manager to define KPIs from scratch.
12. How do you prioritise analysis requests when multiple stakeholders are asking for different things at the same time?

These three STAR-format answers cover the most commonly asked behavioural and technical questions.

Q: You notice a sudden drop in completed visa applications on a particular day. How do you investigate?

*Situation:* At my previous company, a key conversion metric dropped noticeably overnight and the product team flagged it to me early the next morning.

*Task:* I needed to determine quickly whether this was a data pipeline failure, a product bug, or a real drop in user behaviour, and share findings before the business day got underway.

*Action:* I started by checking whether raw event data was still flowing in, comparing row counts in our ingestion tables against the same day in the previous week. Once I confirmed the data was healthy, I sliced the drop by device type, geography, and acquisition channel. The drop was concentrated among mobile Android users from one specific country. I then cross-referenced with the engineering deployment log and found a frontend release had gone out the previous evening.

*Result:* I shared the likely root cause with the product and engineering teams within a few hours. Engineering rolled back the release and the metric recovered. As a follow-up, we added automated anomaly alerts on that funnel step so similar issues would be caught faster going forward.

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Q: Tell me about a time you presented a data finding that changed a business decision.

*Situation:* Our growth team believed that users who applied for multiple visa types in a single session were our most valuable segment. They were planning a campaign to acquire more of these users.

*Task:* I was asked to validate this assumption before the campaign budget was committed.

*Action:* I pulled cohort data and compared retention and repeat application rates across segments. The multi-visa-session group showed high initial activity but lower repeat rates than users who completed a single application successfully on their first try. I built a clear visualisation showing both segments' behaviour over several months and presented it to the growth lead with a concrete recommendation to shift focus.

*Result:* The team pivoted the campaign to target first-time completers instead. Our internal numbers moved in the right direction over the following quarter, and the growth lead said this analysis had saved the team from spending budget on a segment that looked strong on the surface but did not convert into loyal users.

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Q: Describe a time you worked with a product manager to define KPIs from scratch.

*Situation:* A team I worked with launched a new feature that let users track their application status in real time. No one had agreed on what success looked like for this feature before it went live.

*Task:* The PM and I needed to align on KPIs before the first post-launch review meeting, which was coming up in a matter of weeks.

*Action:* I started by asking what business outcome the feature was meant to drive: reducing support tickets, improving retention, or increasing referrals? The PM confirmed the primary goal was fewer inbound queries about application status. I proposed three metrics: the share of users who opened the tracker at least once after applying, the change in support ticket volume for status-related queries, and session frequency of tracker users versus non-tracker users. I also set up a simple dashboard so the PM could monitor these daily without coming to me.

*Result:* We went into the review meeting with agreed-upon metrics and a clear story. Support ticket volume for status queries fell measurably in the first month, giving the PM concrete evidence to present to leadership.

For SQL and technical questions, think out loud. State your assumptions first (what does the schema look like, what counts as 'completed'), then write the query step by step. Interviewers care as much about your reasoning as your syntax.

For product metric questions, use the funnel. Acquisition leads to activation, activation leads to conversion, conversion leads to retention. Anchor your answer in one of these stages before going deeper.

For 'how would you investigate' questions, use a three-step approach: first rule out data pipeline issues, then segment by dimension (device, geography, channel, time of day), then form a hypothesis and check it against external factors like deployments or marketing changes.

For behavioural questions, use STAR (Situation, Task, Action, Result). Keep the Situation short, spend most of your time on Action, and always end with a concrete Result. Avoid vague outcomes like 'it went well' and say what specifically improved or changed.

For A/B testing questions, cover these points in order: what hypothesis you are testing, how you would split the groups, what metric is the primary success indicator and what are the guardrail metrics, and how long you would run the test before reading results.

Atlys is a fast-moving startup, so interviewers are typically looking for analysts who can work with ambiguity and move quickly rather than waiting for perfect data.

Product sense matters as much as SQL. You will be expected to connect a number to a business outcome, not just report it. Candidates who can say 'this metric dropped because of X, and here is what we should do about it' stand out over those who can only describe what the data shows.

Communication is tested directly. Candidates report that interviewers often ask follow-up questions specifically to see if you can explain your thinking simply. Avoid jargon. If you would not say it to a non-technical stakeholder, do not say it in the interview.

Ownership signals matter. Atlys interviewers typically want to see that you have taken initiative in past roles: built something from scratch, flagged a problem before being asked, or pushed back on a flawed assumption with data. Frame your STAR answers around actions you personally drove.

Attention to data quality counts. Because Atlys handles international data from multiple sources, expect probing questions about how you handle messy or incomplete data. Show that your first instinct is to question the data before drawing conclusions.

Week 1: Technical foundation. Practise SQL window functions, CTEs, and aggregations using a dataset that resembles a transaction or event log. Atlys's core data is likely structured around user sessions and application events, so practise writing queries on tables with a user ID, event type, timestamp, and status column.

Week 2: Product and metric thinking. Go through the actual Atlys visa application flow as a user. Note every step where someone could drop off. Think about what data each step would generate and what metrics you would track. Study funnel analysis and cohort analysis if these are not already familiar.

Week 3: Behavioural prep. Write out three to five STAR stories from your past work, each showing a different skill: one for technical problem-solving, one for stakeholder communication, one for handling ambiguity or messy data. Practise saying each one aloud so it sounds natural.

Week 4 (final stretch): Mock rounds and research. Do at least two timed mock SQL rounds. Research Atlys on LinkedIn and in recent news to understand their current focus areas. Prepare three thoughtful questions for the interviewer, ideally about how the data team partners with product or what their biggest analytical challenges are right now.

If your job search is running in parallel with your prep, knok checks 150+ job sites nightly, applies to roles that match your resume, and messages HR for you, so your search keeps moving in the background.

Jumping to a business reason before checking the data. Many candidates, when asked about a metric drop, immediately offer a business explanation. Interviewers at product companies like Atlys want you to first rule out data pipeline issues before explaining the 'why'.

Writing SQL that works but does not scale. Avoid correlated subqueries and Cartesian joins in live coding rounds. Even if the query produces the right answer on a small dataset, interviewers notice whether you are thinking about performance.

Being vague in STAR answers. Saying 'I improved the dashboard' is not enough. Mention what you changed, why it mattered, and what the outcome was. If you do not have a specific number, describe the qualitative change clearly.

Ignoring the business context. When asked a metric or product question, candidates sometimes go straight into technical detail without anchoring in what the business is trying to achieve. Always state the goal first.

Not asking clarifying questions. Jumping into a query without asking about the schema, expected output, or edge cases is a red flag. Interviewers want to see how you scope a problem before you solve it.

Overlooking data quality. Atlys works with data from many countries and partners. When asked to analyse a dataset, always call out assumptions you are making about completeness or consistency. Candidates who skip this step look like they would draw incorrect conclusions in a real setting.