Juspay Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Juspay hiring for Data Analytics roles
Juspay is a Bengaluru-based payments technology company that builds payment orchestration, checkout and UPI infrastructure for merchants and banks. Its products help businesses manage multiple payment gateways and improve transaction success rates. It is known for using functional programming languages such as Haskell and PureScript, and hires software engineers and product engineers.
For Data Analytics roles, this Juspay practice set focuses most on Data Cleaning (6), Python for Analysis (5), Tableau (4), Power BI (4). It has 2 easy, 31 medium, 7 hard questions, so you can start easy and work up to the harder ones.
Company type
Fintech
Headquarters
Bengaluru, India
Main India offices
Bengaluru
Juspay Data Analytics interview process
Online coding assessment or hackathon-style challenge
Technical round 1 (problem solving)
Technical round 2 (design or functional programming)
Hiring manager and culture round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
Write SQL to calculate open rate and click-through rate for each email campaign.
SELECT
s.campaign_id,
COUNT(DISTINCT s.user_id) AS delivered,
COUNT(DISTINCT o.user_id) AS opened,
COUNT(DISTINCT c.user_id) AS clicked,
ROUND(100.0 * COUNT(DISTINCT o.user_id) / COUNT(DISTINCT s.user_id), 1) AS open_rate,
ROUND(100.0 * COUNT(DISTINCT c.user_id) / COUNT(DISTINCT s.user_id), 1) AS ctr
FROM email_sends s
LEFT JOIN email_opens o ON o.campaign_id = s.campaign_id AND o.user_id = s.user_id
LEFT JOIN email_clicks c ON c.campaign_id = s.campaign_id AND c.user_id = s.user_id
WHERE s.status = 'delivered'
GROUP BY s.campaign_id;
02
A file has dates in mixed formats, some dd/mm/yyyy and some mm/dd/yyyy. How do you handle it?
Find out which system or region produced each format, since a source column often explains it. Values with a day above 12 reveal the format unambiguously. Parse with an explicit format per source, for example pd.to_datetime(col, format='%d/%m/%Y', errors='coerce'), rather than letting the tool guess. Check failed or suspicious parses, such as future dates, and ask the source team to send ISO format yyyy-mm-dd going forward.
SELECT region,
SUM(CASE WHEN quarter = 'Q1' THEN amount ELSE 0 END) AS q1,
SUM(CASE WHEN quarter = 'Q2' THEN amount ELSE 0 END) AS q2,
SUM(CASE WHEN quarter = 'Q3' THEN amount ELSE 0 END) AS q3,
SUM(CASE WHEN quarter = 'Q4' THEN amount ELSE 0 END) AS q4
FROM sales
GROUP BY region;
-- conditional aggregation works in every database; some also offer PIVOT
A chi-square test of independence checks whether two categorical variables are related, using a contingency table of counts. Example: is preferred payment method related to city tier? It compares observed counts with the counts expected if there were no relationship. Use it with counts, not percentages, and make sure expected counts are not too small, roughly at least 5 per cell. A significant result shows association, not causation.
06
A stakeholder wants a 40-column crosstab in Tableau that looks exactly like their Excel sheet. How do you handle it?
I would ask what they do with it. If they export and work on it in Excel, a crosstab view with a download option or a scheduled extract is fine, and Tableau should not fight Excel. If they actually scan it for exceptions, I would suggest a summary view with highlights and a drill-down to the detail table. Often both: a short visual summary on top and the detailed table on a second tab.
07
Write Python code to calculate the sample size per group for a conversion test with a 4% baseline and a 10% relative lift.
Keep a cleaning log or README listing each rule, why it exists and its impact, for example removed 1,240 test orders flagged by email domain. Code-based cleaning in SQL, Python or Power Query steps is itself documentation if steps are named clearly. Record counts before and after each major step. This lets reviewers trust the numbers, helps a colleague rerun the work, and answers the question why your total differs from the raw file.
09
What is the grain of a fact table and why should you decide it first?
The grain is the exact meaning of one row in a fact table, such as one order line, one daily store total or one monthly account balance. You decide it first because it controls which dimensions can join, which measures are valid and how totals behave. Mixing grains in one table, for example order lines and order-level shipping charges, causes double counting. Always state the grain in one sentence before adding columns.
Broadcasting lets NumPy perform operations on arrays of different shapes without copying data, by virtually stretching the smaller array. For example, subtracting a 1D array of column means from a 2D array centres every column in one line: X - X.mean(axis=0). Shapes are compatible when trailing dimensions are equal or one of them is 1. It is a key reason vectorised code is much faster than Python loops.
11
How do you present insights to non-technical stakeholders?
Lead with the answer, use one simple chart per point, translate numbers into business impact (₹, %), avoid jargon and end with a clear recommendation and next step.
12
You must present to the sales team that they missed their quarterly target by 18%. How do you handle it?
Lead with the fact clearly, then show where the gap came from, such as which regions, products or deal stages, so it becomes actionable rather than a blame session. Separate controllable causes like lower conversion from uncontrollable ones like a delayed product launch. Highlight what did work, and end with two or three specific actions and the metrics to track them. Share the numbers with sales leaders privately first, to avoid surprises.
Tableau is a visual analytics tool for building interactive charts and dashboards by dragging fields onto shelves. Tableau Desktop is used to author workbooks, Tableau Prep cleans and shapes data, Tableau Server and Tableau Cloud host and share dashboards with security and scheduled refresh, and Tableau Public is a free version where work is saved publicly. Analysts usually build in Desktop and publish to Server or Cloud.
14
How would you present the same churn analysis to the CEO and to the customer support team?
For the CEO, lead with the headline and money: churn rose 2 points, costing about 3 crore annually, mainly from one segment, with two recommended actions and their expected impact, all on one or two slides. For the support team, go deeper into drivers they control: ticket types, resolution times and specific customer pain points, with operational next steps and how progress will be tracked. Same data, different level and call to action.
It extracts and transforms data using recorded steps in M language, such as merging, unpivoting, changing types and removing errors, before loading to the model.
16
What is the unit of randomisation in an experiment, and how do you choose it?
The unit of randomisation is what gets assigned to control or treatment: a user, a session, a device, a store or a city. Usually it should be the user, so one person always sees the same version. Session-level randomisation can show one user both versions and contaminate results. Your analysis unit should match it; if you randomise by user but analyse per page view, standard errors become too small.
A function that calculates over a set of rows related to the current row without collapsing them, such as ROW_NUMBER, RANK, LAG or SUM() OVER (PARTITION BY ...).
18
When would you drop missing values, and when would you impute with mean or median?
Drop rows when few are missing, roughly under 5 percent, and missingness looks random. Impute when dropping would lose too much data. Use the median for skewed numbers such as income or order value, the mean for roughly symmetric data, and the mode or an Unknown category for categoricals. Group-wise imputation, such as median by city, is better than one global value. Always add a flag column showing what was imputed.
19
Average delivery time improved by 4 minutes, but customer complaints about late deliveries went up. How do you investigate?
An average can improve while the tail gets worse. I would look at the distribution: median, 90th and 95th percentiles and the share of orders beyond the promised time. Then break it down by city, time slot and restaurant to see where late orders cluster. Also check whether promised times changed, whether complaint tracking changed and whether a few very fast orders pulled the mean down.
20
How do you remove duplicates in Excel but keep the latest record?
Sort by date (newest first), then Data → Remove Duplicates on the key column — Excel keeps the first occurrence. Or use UNIQUE / a COUNTIFS helper column.
Vanity metrics look impressive but do not help decisions or reflect real value, such as total app downloads, total registered users or social media likes. They usually only go up. Better alternatives are actionable metrics such as active users, retention by cohort, conversion rate and revenue per user. A quick test: if the number changed, would anyone do anything differently? If not, it probably does not belong on the main dashboard.
Build PivotTables from a clean table, add PivotCharts and slicers, link KPIs with formulas, and refresh from Power Query so everything updates together.
24
Sales data from different countries mixes currencies and some weights are in grams while others are in kilograms. How do you handle it?
Never add mixed units. Identify the unit for each row from a currency or unit column, or infer it from the source system. Convert to one standard, for example INR using a dated exchange rate table joined on transaction date, and kilograms using a conversion factor. Keep the original value and unit columns for audit. Document which rates were used, and flag rows where the unit could not be determined.
SUMIFS sums values meeting several criteria and is fast. SUMPRODUCT multiplies arrays and adds them, handling more complex conditions and weighted totals.
27
What is the difference between a t-test and a z-test?
Both compare means. A z-test assumes the population standard deviation is known or the sample is large, so the normal distribution applies. A t-test estimates standard deviation from the sample and uses the t-distribution, which has heavier tails for small samples. In practice analysts mostly use t-tests: one-sample, independent two-sample (Welch's version if variances differ) or paired, for before-after measurements on the same users.
A central fact table (sales, numeric events) linked to dimension tables (date, product, customer). It keeps Power BI models simple, fast and correct when filtering.
29
When would you choose Polars or DuckDB over pandas?
Choose them when data is large, roughly several gigabytes, or when pandas is slow or runs out of memory. DuckDB runs fast SQL directly on CSV and Parquet files on a laptop, ideal for analysts comfortable with SQL. Polars is a DataFrame library with lazy evaluation and multi-threading, often many times faster than pandas. pandas still wins for small data, its huge ecosystem and compatibility with most tutorials and libraries.
30
Your Sales table has OrderDate and ShipDate. How do you analyse sales by ship date when only OrderDate is the active relationship?
-- create an inactive relationship Sales[ShipDate] -> 'Date'[Date] in the model
Sales by Ship Date =
CALCULATE(
[Total Sales],
USERELATIONSHIP(Sales[ShipDate], 'Date'[Date])
)
-- the measure uses the inactive relationship only inside this CALCULATE
-- the alternative is a second role-playing date table
31
A stakeholder wants 20 more visuals added to an already crowded dashboard page. How do you respond?
I would ask which decisions each new visual supports and how often they will look at it. Usually a few are essential and the rest are nice to have. I would propose keeping the main page focused on core KPIs and moving detail into drill-through pages, tooltips or a separate analysis tab. Showing a quick mock-up of both versions helps them see that the cleaner page answers questions faster.
32
What are parameters in Tableau and how do you use them?
A parameter is a single user-controlled value, such as a number, date or string, that is independent of the data. On its own it does nothing; you reference it in calculations, filters, reference lines or bins. Common uses are a Top N selector, a what-if growth rate, or a measure switcher where a CASE statement picks Sales, Profit or Quantity based on the parameter. Parameter actions let users set it by clicking a mark.
33
Write pandas code to calculate customer acquisition cost and payback period in months for each channel.
Reconcile against the source: compare row counts, total amount, and counts by day and by store between source and target. Check for duplicate keys, nulls in mandatory columns, and dates outside the expected month. Compare with the previous month to spot big unexplained jumps. Spot-check a few individual transactions end to end. Automate these checks so each future load produces a small pass or fail report.
A tool that summarises large tables by dragging fields into rows, columns, values and filters, so you can slice sales by region or month without writing formulas.
37
What is columnar storage and why do analytical warehouses use it?
Columnar storage keeps all values of one column together on disk instead of storing full rows together. Analytical queries usually read a few columns across millions of rows, so the engine reads only those columns. Similar values sit side by side and compress very well, which cuts storage and scan cost. The trade-off is slower single-row inserts and updates, which is why OLTP databases stay row-based.
A calculated column is computed row by row and stored in the model. A measure is computed at query time using the filter context, e.g. Total Sales = SUM(Sales[Amount]). Prefer measures for aggregations.
39
Your Tableau numbers change unexpectedly when a filter is applied to a view that uses a FIXED LOD. Why?
Usually it is the order of operations. FIXED LODs are computed before dimension filters, so they ignore a normal region filter, while other measures in the view respect it. That mix makes percentages and totals look inconsistent. Decide what the business wants: if the LOD should respect the filter, add the filter to context; if it should not, keep it and label it clearly, for example share of national sales.
MCAR, missing completely at random, means missingness is unrelated to anything, like a random sensor glitch; dropping rows is mostly safe. MAR, missing at random, means missingness depends on other observed variables, for example older customers skipping the email field; impute using those variables. MNAR, missing not at random, means it depends on the missing value itself, like high earners hiding income; this biases results and needs careful handling or extra data.
Tips to crack the Juspay Data Analytics interview
Practise DSA problems seriously, since Juspay hiring challenges are known to be competitive and focused on problem-solving ability.
Learn functional programming basics like immutability, pure functions and recursion, and try a little Haskell or PureScript.
Understand UPI and card payment flows, including authorisation and settlement, to discuss the domain with confidence.
Keep a one-minute story ready for every project on your CV: problem, your part, tools, result.
Practise the hard questions on this page aloud; Juspay interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Juspay Data Analytics interview?
Candidates usually go through 4 stages: Online coding assessment or hackathon-style challenge; Technical round 1 (problem solving); Technical round 2 (design or functional programming); Hiring manager and culture round. The exact rounds change with the role, team, location and your experience, so confirm the process with the recruiter.
Which topics should I prepare for the Juspay Data Analytics interview?
This practice set leans on Data Cleaning (6), Python for Analysis (5), Tableau (4), Power BI (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Juspay interviewers ask you to walk through real work.
Are these the exact questions asked at Juspay?
No. These are curated practice questions that match the skills and difficulty usually tested in fintech interviews like Juspay's. They are not a leaked or verified list of past questions.
Where does Juspay hire Data Analytics professionals in India?
Juspay has large teams in Bengaluru. Openings and locations change often, so check the Juspay careers page and filter by city and experience.