01
What is a pivot table and when do you use it?
Show answer
A pivot table summarises large data by dragging fields into rows, columns, values and filters — for example total sales by region and month — without writing formulas.
Data Analytics Interview Questions for Fintech Companies
40 questions weighted to the interview style of fintech companies — 6 easy, 32 medium, 2 hard.
How fintech companies interview for Data Analytics
Fintech companies care about correctness, security and scale because they move money. Interviews add domain questions on payments, risk, reconciliation and compliance to the usual technical rounds, and often include practical tasks.
This set focuses on Data Cleaning (8), Excel (5), Tableau (5), Statistics and Case Studies (5).
HR round Hiring manager round Online coding assessment Technical round 1 (DSA) Online assessment or assignment
Solved — fintech companiesStart practicing
3 QUESTIONS FREESign up to unlock the full question bank and track your progress.Unlock every question and answer, then continue practising company-wise and package-wise.
3 questions free Unlock the rest in seconds
02
Analytics shows 1,820 purchases last month but the order database shows 2,150. Which is right, and how do you explain the gap?
Show answer
The order database is the source of truth for counts and revenue. Analytics usually undercounts because of ad blockers, declined cookie consent, users closing the page before the confirmation tag fires, and payment redirects that break sessions. It can also overcount from duplicate tags on page refresh. Quantify the gap per month; if it is stable around 15%, use analytics for channel mix and trends and the database for totals.
03
Write a DAX measure for cumulative sales from the start of the data to the selected date.
Show answer
Cumulative Sales =
VAR maxDate = MAX('Date'[Date])
RETURN
CALCULATE(
[Total Sales],
FILTER(ALL('Date'[Date]), 'Date'[Date] <= maxDate)
)
-- for a running total that resets each year use TOTALYTD([Total Sales], 'Date'[Date])
04
What are parameters in Tableau and how do you use them?
Show answer
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.
05
The column names in a monthly vendor file keep changing. How do you make your cleaning pipeline robust?
Show answer
Agree a data contract with the vendor if possible. In the pipeline, normalise headers by trimming, lower-casing and replacing spaces, then map known variants to standard names through a lookup table you maintain. Validate that all required columns exist after mapping and fail with a clear message if not, instead of producing silent blanks. Select columns by name, never by position, and log any unexpected new columns for review.
06
How do you let users choose Top N customers with a parameter in Tableau?
Show answer
Create an integer parameter Top N with a range of 5 to 50 and show its control. Drag Customer to the Filters shelf, choose the Top tab, select By field, and set it to top Top N by SUM(Sales). Alternatively create a set based on the parameter so you can show Top N versus Others in one chart. If the Top N must respect a category filter, add that category filter to context.
07
Explain INDEX and MATCH together.
Show answer
MATCH finds the position of a value in a range and INDEX returns the item at that position, giving a flexible lookup that works left or right and survives column insertions.
08
What is a percentile and what is the interquartile range?
Show answer
A percentile is the value below which a given percentage of data falls; the 90th percentile delivery time means 90 percent of deliveries were faster. The interquartile range is the 75th percentile minus the 25th percentile, the spread of the middle half. IQR is robust to outliers, and values beyond 1.5 times the IQR from the quartiles are often flagged as outliers in box plots.
09
What is broadcasting in NumPy?
Show answer
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.
10
How does the FILTER function work in Excel? Write a formula to list all North region orders.
Show answer
-- A2:D500 holds Order, Customer, Region, Amount
=FILTER(A2:D500, C2:C500="North", "No orders")
-- two conditions (AND) multiply the tests
=FILTER(A2:D500, (C2:C500="North")*(D2:D500>10000), "No orders")
-- OR conditions add the tests
=FILTER(A2:D500, (C2:C500="North")+(C2:C500="East"))
11
Write pandas code to standardise inconsistent city names such as 'delhi ', 'New Delhi' and 'DELHI'.
Show answer
city_map = {
'delhi': 'Delhi',
'new delhi': 'Delhi',
'bombay': 'Mumbai',
'mumbai': 'Mumbai',
'bengaluru': 'Bengaluru',
'bangalore': 'Bengaluru',
}
clean = df['city'].str.strip().str.lower().str.replace(r'\s+', ' ', regex=True)
df['city_clean'] = clean.map(city_map).fillna(clean.str.title())
print(df['city_clean'].value_counts())
12
Which KPIs would you include in an HR attrition dashboard?
Show answer
Core KPIs: headcount, monthly and annualised attrition rate, voluntary versus involuntary split, and early attrition within the first 90 days or first year. Breakdowns by department, location, manager, tenure band, performance rating and salary band show where it concentrates. Add hiring metrics such as time to fill and offer acceptance rate, and exit reasons from exit interviews. Protect privacy by hiding very small groups.
13
What is the difference between sets and groups in Tableau?
Show answer
A group combines dimension members into higher-level categories, such as merging small states into Others; it is static and each member belongs to one group. A set splits members into In and Out based on a selection or condition, such as top 10 customers by sales, and can be dynamic and recalculate with data. Sets can be combined, used in calculations and changed by set actions, making them more powerful for interactive analysis.
14
Write SQL to find the number of days between each customer's consecutive orders and their average gap.
Show answer
WITH gaps AS (
SELECT customer_id,
order_date,
order_date - LAG(order_date) OVER (
PARTITION BY customer_id ORDER BY order_date) AS days_since_prev
FROM orders
)
SELECT customer_id,
COUNT(*) AS orders,
AVG(days_since_prev) AS avg_gap_days
FROM gaps
GROUP BY customer_id
HAVING COUNT(*) > 1;
-- order_date is DATE (PostgreSQL); use DATEDIFF in other databases
15
Write pandas code to trim whitespace and lower-case every text column in a DataFrame.
Show answer
text_cols = df.select_dtypes(include='object').columns
for col in text_cols:
df[col] = (
df[col]
.astype('string')
.str.strip()
.str.lower()
.replace('', pd.NA)
)
# also tidy column names
df.columns = df.columns.str.strip().str.lower().str.replace(' ', '_')
16
How do you merge two DataFrames on different column names?
Show answer
pd.merge(df1, df2, left_on='cust_id', right_on='id', how='left') joins on differently named keys.
17
What is the difference between a t-test and a z-test?
Show answer
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.
18
How should you document data cleaning decisions?
Show answer
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.
19
What is Power Pivot and the Excel Data Model?
Show answer
The Data Model is an in-memory columnar store inside Excel that holds several related tables, and Power Pivot is the interface to manage it. Instead of flattening data with VLOOKUPs, you load fact and dimension tables, create relationships and write DAX measures. It handles millions of rows that would not fit on a sheet, and PivotTables built on it can slice across tables. It is the same engine idea that Power BI uses.
20
What do COALESCE and NULLIF do in SQL?
Show answer
COALESCE returns the first non-null value from its arguments, for example COALESCE(phone, alt_phone, 'Not given') fills missing phone numbers. NULLIF(a, b) returns NULL when a equals b, otherwise a. Its classic use is avoiding division by zero: revenue / NULLIF(orders, 0) returns NULL instead of an error when orders is zero. Together they handle most null-related reporting problems cleanly.
21
How do you handle errors like #N/A and #DIV/0!?
Show answer
Fix the root cause when possible, or wrap formulas with IFERROR or IFNA to return a clear fallback value.
22
Why do ratio metrics need extra care on a dashboard compared with count metrics?
Show answer
A ratio like conversion rate or average order value can move because the numerator or the denominator changed, so always show both components nearby. Ratios can look dramatic on small bases, such as 2 of 4 users. They also cannot be averaged across segments; you must divide total numerator by total denominator, otherwise small segments get too much weight. Counts are simpler but need context like trends or targets.
23
How do you connect Python to a SQL database?
Show answer
Create an engine with SQLAlchemy or a driver such as psycopg2, then run pd.read_sql(query, engine) to load results into a DataFrame.
24
How do you detect and handle skewed data?
Show answer
Check histograms and skew(), then apply log, square-root or Box-Cox transforms, or use medians and robust statistics.
25
What is the difference between relationships, joins and data blending in Tableau?
Show answer
Relationships, the default logical layer, connect tables flexibly and Tableau decides join type and level per view, avoiding duplicated measures. Joins physically merge tables into one at a fixed level, which can duplicate rows if grains differ. Blending combines data from separate data sources at the view level using a primary and secondary source, aggregating the secondary first. Prefer relationships, use joins for specific row-level needs, and blending as a last resort.
26
What is the difference between an outlier and a data error?
Show answer
An outlier is a genuine but unusual value, such as a bulk corporate order of 50 laptops; it is real and may be important. A data error is an incorrect value, such as an age of 250 or a price entered in paise instead of rupees. Errors should be fixed or removed. Outliers should be investigated and often kept, perhaps analysed separately or with median-based metrics so they do not distort averages.
27
A stakeholder asks for a pie chart of 12 product categories. When is a pie chart acceptable, and what would you suggest?
Show answer
A pie chart works only for showing parts of a whole with very few categories, two to four, where one share clearly dominates, such as 70 percent of orders are prepaid. Avoid it when there are many slices, when values are close, or when comparing across periods, because people judge angles poorly. A sorted bar chart is almost always easier to read and compare. Never use 3D pies.
28
What is the difference between a view and a materialised view?
Show answer
A view stores a query and runs it each time; a materialised view stores the result physically and must be refreshed, making reads faster.
29
Your manager wants a visual that always shows the top 5 products for whatever region is selected in a slicer. How do you build it?
Show answer
The simplest way is a visual-level Top N filter on Product, by the Total Sales measure, set to 5; it recalculates for every slicer selection. If the number must be user selectable, create a numeric what-if parameter and a ranking measure using RANKX over ALLSELECTED(Product), then filter the visual where rank is less than or equal to the parameter. I would also add an Others row if the business wants totals to still match.
30
What steps would you apply in Power Query to clean a messy system export?
Show answer
Typical steps: remove top junk rows and promote the real header row, remove blank rows and columns, set correct data types, trim and clean text, split combined columns such as Name - City, replace error values, fill down for grouped labels, remove duplicates on the business key, and unpivot wide month columns. Rename steps meaningfully so others can follow them, and keep the query foldable when the source is a database.
31
Write pandas code to convert daily sales into weekly totals, showing zero for days with no sales.
Show answer
df['date'] = pd.to_datetime(df['date'])
daily = (df.groupby('date')['sales'].sum()
.asfreq('D', fill_value=0)) # adds missing days as 0
weekly = daily.resample('W-MON', label='left', closed='left').sum()
print(weekly.head())
# W-MON with label/closed left gives weeks starting on Monday
32
Your findings deck has 30 slides and you have 10 minutes with leadership. How do you cut it down?
Show answer
Write the single key message first, then keep only slides that directly support it, usually three to five: the headline finding, the main driver, the impact in money, and the recommendation with next steps. Merge related charts, use action titles, and move everything else to an appendix for questions. Rehearse to fit in about seven minutes, leaving time for discussion, because the discussion is where decisions are made.
33
A junior analyst wants to load one huge flat table with every column into Power BI instead of a star schema. How do you advise them?
Show answer
A flat table is quick to start and fine for a small one-off analysis. But at scale it bloats the model because repeated text attributes compress poorly, makes DAX filters and time intelligence harder, and breaks when you add a second fact like targets or returns. I would show them a star schema with one fact and a few dimensions, compare model size and measure speed, and let the results convince them.
34
An inner join between orders and customers returns fewer orders than the orders table has. What could be wrong?
Show answer
Orphan records are child rows whose foreign key has no matching parent, such as orders with a customer_id that does not exist in the customer table. They break joins silently: an inner join drops them and totals fall short. Find them with a LEFT JOIN from the child to the parent and filter where the parent key IS NULL. Then fix the source, add an Unknown member, or report them separately.
35
How do you clean messy text data in Excel?
Show answer
Use TRIM, CLEAN, UPPER or PROPER, Text to Columns, Flash Fill, SUBSTITUTE, and Power Query for repeatable cleaning steps.
36
What is the Central Limit Theorem and why does it matter for analysts?
Show answer
The Central Limit Theorem says that the distribution of sample means approaches a normal distribution as sample size grows, usually from about 30 observations, even if the underlying data is skewed. This is why we can use z-tests, t-tests and confidence intervals for averages such as order value or session time. It does not make the raw data normal, and very heavy-tailed data may need larger samples.
37
What are leading and lagging indicators? Give examples.
Show answer
Lagging indicators report outcomes that have already happened, such as monthly revenue, profit or churn; they are accurate but too late to change. Leading indicators move earlier and predict outcomes, such as qualified leads, demo bookings, cart additions or support ticket backlog. A sales dashboard should pair them: pipeline coverage today signals revenue next quarter. Leading indicators need validation that they really predict the lagging result.
38
Your Top 10 products filter in Tableau ignores the category filter. Why, and how do you fix it?
Show answer
A context filter is applied before other dimension filters and before FIXED LOD calculations, creating a smaller dataset the rest of the view works on. Use it when a Top N filter should operate within a selected category, for example top 10 products within Electronics, or when a FIXED LOD must respect a filter. Add a filter to context from its right-click menu; it turns grey. Too many context filters can hurt performance.
39
Average delivery time improved by 4 minutes, but customer complaints about late deliveries went up. How do you investigate?
Show answer
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.
40
What is a date table in Power BI and why should you mark it as one?
Show answer
A date table has one row per day with no gaps, covering the full range of your data, plus columns like year, month, quarter and week. Marking it as a date table tells Power BI to use it for time intelligence functions such as TOTALYTD and SAMEPERIODLASTYEAR. Without it, those functions can give wrong results and auto date tables bloat the model. Relate it to every fact table's date column.
Fintech companies (19)
Fintech
Intuit
Data Analytics interview questions
Bengaluru
View questionsFintech
CRED
Data Analytics interview questions
Bengaluru
View questionsFintech
Paytm
Data Analytics interview questions
Noida, Bengaluru, Mumbai
View questionsFintech
PhonePe
Data Analytics interview questions
Bengaluru, Pune
View questionsFintech
Razorpay
Data Analytics interview questions
Bengaluru, Mumbai
View questionsFintech
PayPal
Data Analytics interview questions
Chennai, Bengaluru, Hyderabad
View questionsFintech
Stripe
Data Analytics interview questions
Bengaluru
View questionsFintech
Visa
Data Analytics interview questions
Bengaluru, Mumbai
View questionsFintech
Mastercard
Data Analytics interview questions
Pune, Vadodara, Gurugram +1
View questionsFintech
Bloomberg
Data Analytics interview questions
Mumbai
View questionsFintech
DE Shaw
Data Analytics interview questions
Hyderabad, Bengaluru, Gurugram
View questionsFintech
Tower Research Capital
Data Analytics interview questions
Gurugram
View questionsFintech
Groww
Data Analytics interview questions
Bengaluru
View questionsFintech
Zerodha
Data Analytics interview questions
Bengaluru
View questionsFintech
Policybazaar
Data Analytics interview questions
Gurugram
View questionsFintech
Juspay
Data Analytics interview questions
Bengaluru
View questionsFintech
Cashfree Payments
Data Analytics interview questions
Bengaluru
View questionsFintech
Pine Labs
Data Analytics interview questions
Noida, Bengaluru
View questionsFintech
Bajaj Finserv
Data Analytics interview questions
Pune
View questionsFrequently asked questions
How are Data Analytics interviews at fintech companies different?
Fintech companies care about correctness, security and scale because they move money. Interviews add domain questions on payments, risk, reconciliation and compliance to the usual technical rounds, and often include practical tasks.
Which fintech companies are covered?
This page links to 19 fintech companies, including Intuit, CRED, Paytm, PhonePe, Razorpay, PayPal, Stripe, Visa. Each has its own page with rounds and a separate practice set.
What rounds should I expect?
Common rounds at these companies include: HR round; Hiring manager round; Online coding assessment; Technical round 1 (DSA); Online assessment or assignment. The exact process varies by company and role.
Other company types
More ways to practise
Company-wise Data Analytics questionsPackage-wise Data Analytics questionsExperience-wise Data Analytics questionsRole-wise Data Analytics questionsCity-wise Data Analytics questionsMost asked Data Analytics questionsScenario-based questionsPractical / hands-on questionsHR & behavioural questionsData Analytics mock interviewOnline assessment questionsTechnical round questionsManagerial round questionsData Analytics topic sheets