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

Skeps is a fintech company that powers embedded lending and point-of-sale financing. They connect retailers with lenders so consumers can access credit at checkout, which means the data team works with loan, transaction, and partner performance data every day.

As of July 2026, Skeps has 11 open Data Analyst roles, making it an active hiring target worth pursuing seriously. Candidates typically report 2-3 rounds: a recruiter or HR screen, a technical round covering SQL and analytical reasoning, and a final round with the hiring manager. Some mid-level candidates also report a take-home assignment involving a lending dataset. The full process typically wraps up within a few weeks.

Data Analyst salary bands across India (knok jobradar, July 2026):

Because Skeps handles real credit and partner data, they look for analysts who understand financial metrics and can connect numbers to business decisions, not just candidates who can write queries.

These questions are based on Skeps' fintech domain and commonly reported Data Analyst interview patterns at lending and credit companies.

1. Walk me through how you would analyse loan repayment trends across different customer segments.
2. Write a SQL query to find the top 5 merchants by total loan disbursement value in the past quarter.
3. How would you detect anomalies in transaction data for a point-of-sale lending platform?
4. What metrics would you use to measure the health of a consumer credit portfolio?
5. A key lending partner reports a sudden drop in approval rates. How do you investigate the root cause?
6. How do you handle missing or inconsistent values in a financial dataset before building a report?
7. Explain the difference between cohort analysis and funnel analysis. When would you use each at a lending company?
8. How would you design a dashboard to track repayment default rates for a retail lending product?
9. Tell me about a time you found an insight in data that directly changed a business decision.
10. What analytical tools and languages are you most comfortable with, and how have you applied them to financial data?
11. How would you validate the outputs of a credit risk model built by the data science team?
12. Skeps serves both retailers and lenders. How would you design a reporting framework that meets the needs of both stakeholder groups?

Use the STAR format for every behavioural question: Situation, Task, Action, Result.

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Q: Tell me about a time you found an insight in data that changed a business decision.

*Situation:* I was at an e-commerce company where the sales team believed a specific product category was underperforming because of low traffic.

*Task:* My task was to audit the full acquisition funnel before the team cut marketing spend on that category.

*Action:* I pulled session and conversion data in SQL, segmented by acquisition channel, and found that paid traffic converted well. The problem was that organic traffic had a 'bounce rate' far higher than other categories. I traced it to a mobile page-load issue specific to that category.

*Result:* The team fixed the technical issue instead of cutting spend. Conversion recovered within two weeks, confirming the channel was not the problem.

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Q: How do you handle missing or inconsistent data in a financial dataset?

*Situation:* While building a monthly lending performance report, I noticed a significant portion of rows had null values in the repayment date column.

*Task:* I needed to decide whether to impute, exclude, or flag those rows before the report reached senior stakeholders.

*Action:* I first checked whether the nulls were random or tied to a specific lender or time window. They were clustered around one partner's data feed during a system migration. I excluded those rows from rate calculations, added a clear footnote in the report, and flagged the upstream issue to the engineering team.

*Result:* The report went out with accurate numbers, stakeholders trusted the data, and the pipeline issue was resolved within a week.

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Q: Explain cohort analysis and describe how you have used it.

*Situation:* At a fintech startup, the product team wanted to know whether customers who took a first loan kept engaging with the platform over the following months.

*Task:* I was asked to build a retention analysis to guide decisions on post-loan engagement campaigns.

*Action:* I grouped customers by their first loan month in SQL and tracked activity across each subsequent month. I visualised the result as a heatmap in the BI tool so the product team could see exactly where drop-off was sharpest.

*Result:* The analysis revealed a clear drop-off at month three for a specific segment. The product team launched a re-engagement nudge at month two, and industry surveys suggest targeted nudges of this kind commonly improve short-term retention.

For SQL and technical questions, think out loud. Interviewers at data-heavy fintechs care as much about your reasoning as your final query. State your assumptions upfront (for example: 'I am assuming one row per transaction'), write the query step by step, and call out edge cases like nulls or duplicate records.

For metric and business questions, use a simple structure: define the metric, explain what drives it, describe how you would break it down by dimension (time, segment, partner), and state what action the business could take based on the result. This signals analytical thinking, not just technical ability.

For root-cause investigation questions, narrow from the broad to the specific. Start with total numbers (are they down overall?), then break by dimension (which segment, product, or date range?), then confirm whether it is a data issue or a real business issue. This approach is especially relevant at Skeps, where a dip in one lending metric could have several unrelated causes.

For 'tell me about a time' questions, keep Situation brief and spend most of your time on Action and Result. If you cannot recall a precise number for the result, describe the decision that changed or the problem that was resolved. Honest, grounded answers land better than inflated claims.

Skeps operates in embedded finance, a domain where data accuracy, regulatory compliance, and partner trust matter significantly. Interviewers look for more than raw technical skill.

Domain awareness. You do not need a formal finance background, but you should understand basic lending concepts: approval rate, default rate, repayment schedule, loan tenure, and portfolio aging. Candidates who use this vocabulary naturally stand out from those who treat lending data as just another dataset.

SQL fluency under pressure. Expect to write queries live or on a shared screen. Practice window functions, CTEs, and aggregations on messy multi-table data. Multi-lender and multi-merchant joins are common at a company like Skeps.

Stakeholder communication. Skeps serves both retail partners and financial institutions. Interviewers look for candidates who can translate a data finding into a clear business recommendation, not just a table or chart.

Attention to data quality. In fintech, a wrong number can have compliance implications. Candidates who mention validation checks, source reconciliation, and audit trails consistently score higher than those who jump straight to insights.

Ownership mindset. Smaller fintech teams expect analysts to own a problem from data pull to stakeholder presentation. Answers that show initiative and follow-through signal a strong fit for the team.

Week 1: SQL and technical foundation.
Practice intermediate to advanced SQL each day: window functions (RANK, LAG, LEAD), CTEs, and multi-table joins. Use publicly available datasets with e-commerce or finance themes. Focus on writing clean, readable queries you can explain out loud under pressure.

Week 2: Fintech and lending domain.
Read about how point-of-sale financing works, what metrics lenders track (approval rate, default rate, loan tenure), and how data flows between a retailer, a lending platform, and a bank. You do not need to be an expert. You need to hold a confident conversation about the data you would work with at Skeps.

Week 3: Case practice and communication.
Practice open-ended business questions out loud. Prepare 3-4 strong work examples you can adapt to different STAR questions. If Skeps offers a take-home assignment, invest time in a clean chart, a short executive summary, and well-documented queries.

Before your interview, review Skeps' publicly available product information on embedded lending and retailer partnerships. Prepare 2-3 thoughtful questions about data infrastructure, team structure, or how the analyst role connects to product decisions.

While you are getting interview-ready, knok checks 150+ job sites nightly, applies to jobs matching your resume, and messages HR for you, so you do not miss new Skeps openings while you focus on preparation.

Jumping to SQL without clarifying the question. Candidates often start writing a query before confirming what the interviewer actually wants. A brief clarification saves time and signals good analytical habits.

Ignoring data quality in answers. When asked to analyse a metric, candidates who skip straight to insights without mentioning data validation come across as inexperienced. In fintech, accuracy is non-negotiable, and interviewers notice when candidates treat it as an afterthought.

Giving vague STAR answers. Saying 'I improved the dashboard and the team liked it' is not a result. Push yourself to recall the actual outcome: a decision that changed, a problem that was resolved, or a process that improved. If you cannot quantify it, describe the business impact clearly.

Not knowing your listed tools deeply. If your resume mentions Python, Tableau, or Power BI, expect follow-up questions. Saying 'I have used it but not in depth' for a tool you listed damages credibility. Only include tools you can discuss confidently.

Failing to connect your experience to Skeps' context. Candidates with fintech or financial data experience often give generic answers instead of drawing an explicit connection. Saying 'At my previous role I worked on similar credit portfolio data, specifically...' is far more effective than a response that could apply to any company.