Standard Chartered Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Standard Chartered hiring for Data Analytics roles
Standard Chartered is a British international bank with most of its business in Asia, Africa and the Middle East. Its India global business services centres in Chennai and Bengaluru support technology, operations, analytics and risk. It hires for software engineers, testers, data analysts, data engineers and financial crime analysts.
For Data Analytics roles, this Standard Chartered practice set focuses most on Python for Analysis (6), Data Cleaning (5), SQL (5), Power BI (5). It has 6 easy, 25 medium, 9 hard questions, so you can start easy and work up to the harder ones.
Company type
Banking & finance
Headquarters
London, United Kingdom
Main India offices
Chennai, Bengaluru, Mumbai
Standard Chartered Data Analytics interview process
Online assessment (aptitude + technical)
Technical round
Managerial round
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
Your scheduled Python report failed silently and nobody noticed for a week. How do you make it reliable?
Add logging to a file or central log, wrap the main steps in try and except that send an alert by email or chat on failure, and exit with a non-zero code so the scheduler marks the job failed. Add data checks, such as row counts above a minimum and no missing dates, before sending output. Use a proper scheduler or orchestrator with retries, and include the data date in every report.
02
Write pandas code to trim whitespace and lower-case every text column in a DataFrame.
Additive facts, like sales amount or quantity, can be summed across every dimension. Semi-additive facts, like account balance or stock on hand, can be summed across accounts or stores but not across time; for time you take the closing or average value. Non-additive facts, like percentages, ratios or unit prices, cannot be summed at all, so you store their components (profit and revenue) and calculate the ratio at query time.
04
How would you find customers who never placed an order?
Situation: our operations team assumed late deliveries were evenly spread. Task: I built a delivery performance dashboard. Action: I added a breakdown by courier partner and pin code cluster with a weekly trend, and reviewed it with the operations manager. Result: it showed one partner caused 60 percent of delays in two zones. The company shifted those zones to another partner, and on-time delivery rose from 84 to 92 percent in two months.
Visually with a box plot, with the IQR rule (below Q1 − 1.5×IQR or above Q3 + 1.5×IQR) or a z-score above 3. Then decide with the business whether to cap, remove or keep them.
08
After joining orders to payments, your revenue total doubles. What went wrong?
This is a join fan-out: an order has more than one matching payment row, for example split payments or retries, so each order line is repeated and sums are inflated. Check by counting rows per key on both sides. Fix it by aggregating payments to one row per order before joining, joining at the correct grain, or using the right key. Always compare row counts and totals before and after a join.
09
Your sales data contains negative quantities. What do you do with them?
Do not delete them blindly. First find out what they mean: often they are returns, cancellations or credit notes, which are valid business events. Check with the source team or a documentation note. If they are returns, keep them but report gross sales, returns and net sales separately. If some are clearly entry errors, such as a negative quantity on a new order, flag and correct them and log the rule.
A Data Table shows how a formula's result changes when one or two inputs vary. For example, place a loan EMI formula in a corner, list interest rates down the column and tenures across the row, then use What-If Analysis, Data Table to fill every combination. It is ideal for sensitivity analysis in pricing or budgeting. Large Data Tables slow recalculation, so set calculation to automatic except tables when needed.
11
What is the difference between a clustered and a non-clustered index?
A lambda is a small anonymous function written in one line, for example lambda x: x * 1.18 to add GST. Analysts use lambdas with sorted for custom sort keys, with pandas assign for computed columns, for example assign(margin=lambda d: d.profit / d.sales), and occasionally with apply. For anything longer than one expression or reused in several places, a normal def function is clearer and easier to test.
13
The customer table has near-duplicates like 'Rahul Sharma, 98xxxx1234' and 'Rahul Sharma, +91 98xxxx1234'. How do you deduplicate?
First normalise: trim and lower-case names, collapse spaces, and strip country codes and symbols from phones and emails. Then match on strong keys such as phone or email. For remaining records use fuzzy matching on names with a similarity score, restricted to the same city or pin code to limit comparisons. Review borderline scores manually, keep a mapping from old IDs to a master ID, and fix entry at the source.
14
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?
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.
15
Write a DAX measure for a KPI card showing achievement against target and a colour for conditional formatting.
Achievement % =
DIVIDE([Total Sales], [Sales Target])
KPI Colour =
VAR a = [Achievement %]
RETURN
SWITCH(
TRUE(),
ISBLANK(a), "#9E9E9E",
a >= 1, "#2E7D32",
a >= 0.9, "#F9A825",
"#C62828"
)
-- apply KPI Colour via Format > Callout value > fx > Field value
16
Traffic suddenly doubled from one city, with near-zero engagement time and no conversions. What is happening and what do you do?
This is very likely bot or spam traffic, or a misfiring tag. Check the user agents, IP ranges, hostnames, screen resolutions and landing pages for that traffic. If it is bots, exclude it with filters or data segments in analytics and in your reporting layer, and inform the web team so they can block it. Then restate the affected dashboard numbers, because bots inflate sessions and pull down conversion rate.
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.
19
Write SQL to find users who logged in on at least 3 consecutive days.
WITH d AS (
SELECT DISTINCT user_id, CAST(login_time AS DATE) AS dt
FROM logins
),
g AS (
SELECT user_id, dt,
dt - CAST(ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY dt) AS INT) AS grp
FROM d
)
SELECT DISTINCT user_id
FROM g
GROUP BY user_id, grp
HAVING COUNT(*) >= 3;
-- gaps and islands: consecutive dates share the same grp value
Field parameters let report users switch which measure or dimension a visual uses through a slicer. For example, one chart can switch between Sales, Profit and Quantity, or between grouping by Region, Category and Channel. This replaces many duplicate visuals or bookmark tricks, keeping the report lighter and easier to maintain. They are created from Modeling, New parameter, Fields, which generates a small table behind the slicer.
A north star metric is the single measure that best captures the value a product delivers to customers and predicts long-term growth, such as weekly active learners for an education app or orders delivered for a delivery service. It aligns teams around one outcome. It should be supported by input metrics that teams can influence, such as activation rate and repeat usage, and balanced with guardrails like cost or quality.
22
How do you calculate a simple customer lifetime value?
A simple formula is CLV = average order value x purchase frequency per year x gross margin x expected lifetime in years. Example: 800 rupees x 6 orders x 30 percent margin x 3 years = 4,320 rupees. Expected lifetime can be estimated as 1 divided by annual churn rate. A better version discounts future value and calculates CLV by cohort or segment, because averages hide very different customer groups.
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.
25
A query on a 2-billion-row fact table in a cloud warehouse scans the whole table even when you filter one week. How do you fix it?
Partition the table by date so the engine can prune partitions outside the filtered week, and cluster or sort by frequently filtered columns such as store or customer. Make sure the filter is on the partition column directly, not wrapped in a function like CAST or DATE_TRUNC that blocks pruning. Select only needed columns, because storage is columnar. For dashboards, also consider a pre-aggregated daily summary table.
A conformed dimension is one dimension table with the same keys, attributes and meanings shared by several fact tables. For example, one date and one product dimension used by sales, returns and inventory facts. Because they conform, you can compare sales and returns by the same product category in one report without mismatched definitions. Conformed dimensions are the main way an enterprise warehouse stays consistent across departments.
27
What types of dashboard actions does Tableau offer?
Filter actions filter other sheets when a user selects or hovers on a mark, such as clicking a region to filter a product table. Highlight actions emphasise related marks without removing others. URL actions open a web page with field values in the link. Go to Sheet navigates to another dashboard. Parameter and set actions update a parameter or set based on selection, enabling advanced interactions like dynamic drill-downs and comparisons.
28
How can you automate data quality checks in a reporting pipeline?
Define tests as code that run after every load: not-null and unique checks on keys, accepted value lists, referential integrity, row count within an expected range compared with recent days, and freshness of the latest timestamp. Store results in a table and alert the team on failures before dashboards refresh. Start with checks on the few tables behind key KPIs, then expand. This catches issues before stakeholders do.
29
Leadership wants a single revenue number for next quarter, but your forecast is uncertain. How do you present it?
Show ranges, not just a single number: a forecast line with a shaded prediction interval, or a statement like revenue will likely be between 48 and 55 crore, most likely 51. Explain the main assumptions and what would change the outcome, perhaps with best, base and worst scenarios. Use plain words such as likely or unlikely consistently. Hiding uncertainty makes a forecast look precise but leads to bad decisions when reality differs.
30
Tell me about a time you disagreed with your manager about how to measure something.
Situation: my manager wanted to report average resolution time for support tickets. Task: I felt a few very old tickets distorted the average. Action: I prepared a quick comparison showing mean versus median and the percentage resolved within 24 hours, and explained why the median was more representative. Result: we agreed to show median and the 24-hour rate, keeping the mean in a tooltip, and the team stopped chasing misleading spikes.
First profile to find the slow step instead of guessing. Common fixes: replace row-wise apply and loops with vectorised operations, convert repeated strings to category dtype, downcast numeric types, read only needed columns with usecols, and filter early. Push heavy joins and aggregations into SQL. If it is still slow, use a columnar engine such as DuckDB or Polars, or store intermediate data as Parquet instead of CSV.
33
How would you design an A/B test for a new checkout button?
Define one primary metric, such as checkout conversion, plus guardrails like refund rate. State the hypothesis and minimum detectable effect, then calculate sample size and duration for chosen power and significance. Randomise users, not sessions, into control and variant, check the split is balanced, and run for full weeks to cover weekday patterns. Do not peek and stop early. Finally analyse with a proportion test and check key segments.
34
What is the difference between a calculated table in DAX and a table created in Power Query?
A Power Query table is built during data refresh using M, before data enters the model, and is compressed like any imported table. A DAX calculated table is computed after load from other model tables, using functions such as CALENDAR, SUMMARIZE or DISTINCT. Prefer Power Query for shaping and cleaning source data. Use calculated tables for model-dependent needs, such as a date table from existing data or a small what-if table.
SELECT category,
SUM(amount) AS revenue,
ROUND(100.0 * SUM(amount)
/ SUM(SUM(amount)) OVER (), 2) AS pct_share
FROM sales
GROUP BY category
ORDER BY revenue DESC;
-- SUM(SUM(amount)) OVER () is the grand total across all groups
37
A manager asks for a dashboard that refreshes every minute. How do you decide the right refresh frequency?
Match refresh to how quickly decisions are made. A monthly board pack needs daily refresh at most, while a warehouse dispatch board may need 15-minute or live updates. Faster refresh costs more in source load, licensing and complexity, and can show incomplete data. Ask how quickly someone will act on a change, check when upstream systems finish loading, and always display the last refreshed time on the dashboard.
38
Write an Excel formula to put invoices into ageing buckets (0-30, 31-60, 61-90, 90+ days).
-- C2 = invoice date, TODAY() = as-of date
=LET(age, TODAY()-C2,
IFS(age<=30, "0-30",
age<=60, "31-60",
age<=90, "61-90",
TRUE, "90+"))
-- then a PivotTable with bucket in rows and SUM of amount gives the ageing report
39
What is the difference between a live connection and an extract in Tableau?
A live connection queries the source database every time a user interacts, so data is always current but performance depends on the database. An extract is a compressed snapshot stored in Tableau's hyper format, usually much faster, refreshed on a schedule and usable offline. Use live for near real-time needs on a fast warehouse, and extracts for speed, slow sources or when you want to reduce load on production systems.
40
Write pandas code to extract month name, weekday and hour from a timestamp column.
Tips to crack the Standard Chartered Data Analytics interview
Learn trade finance, cash management and financial crime compliance basics, which are strong areas for the bank.
Revise SQL, Python and testing concepts for technology and analytics roles, using banking transaction examples where possible.
Read about Standard Chartered's valued behaviours, such as Do the right thing and Never settle.
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; Standard Chartered interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Standard Chartered Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + technical); Technical round; Managerial round; HR 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 Standard Chartered Data Analytics interview?
This practice set leans on Python for Analysis (6), Data Cleaning (5), SQL (5), Power BI (5). Revise those first, then practise explaining one or two of your own projects end to end, because most Standard Chartered interviewers ask you to walk through real work.
Are these the exact questions asked at Standard Chartered?
No. These are curated practice questions that match the skills and difficulty usually tested in banking & finance interviews like Standard Chartered's. They are not a leaked or verified list of past questions.
Where does Standard Chartered hire Data Analytics professionals in India?
Standard Chartered has large teams in Chennai, Bengaluru, Mumbai. Openings and locations change often, so check the Standard Chartered careers page and filter by city and experience.