Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About BNY hiring for Data Analytics roles
BNY, formerly known as BNY Mellon, is an American financial services company known for custody, asset servicing, clearing, treasury services and investment management. It is one of the oldest banks in the United States. In India its centres in Pune and Chennai hire software engineers, data analysts, risk professionals and operations roles that support global clients.
For Data Analytics roles, this BNY practice set focuses most on SQL (8), Dashboards and KPIs (5), Tableau (5), Power BI (5). It has 7 easy, 26 medium, 7 hard questions, so you can start easy and work up to the harder ones.
Company type
Banking & finance
Headquarters
New York, USA
Main India offices
Pune, Chennai
BNY Data Analytics interview process
Online assessment (aptitude + coding)
Technical interview
Managerial round
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
Your event logs store timestamps in UTC, but the business reports daily sales in IST, and late-night orders land on the wrong day. How do you fix it?
Convert timestamps to the business time zone before deriving the date. IST is UTC plus 5 hours 30 minutes, so an order at 20:00 UTC belongs to the next day in IST. In SQL use AT TIME ZONE or CONVERT_TZ, and in pandas use tz_localize('UTC') followed by tz_convert('Asia/Kolkata'). Store raw UTC, add a local date column in the model, and document which one each report uses.
02
What is the Central Limit Theorem and why does it matter for analysts?
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.
03
What is the difference between a fact table and a dimension table?
A fact table stores measurable events at a defined grain, such as one row per order line, with numeric measures like quantity and amount plus foreign keys. Dimension tables describe the context of those events: customer, product, store and date, with descriptive attributes used for filtering and grouping. Facts are long and grow quickly; dimensions are wider and smaller. Together they form star or snowflake schemas.
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.
06
Write pandas code to fill missing values in a column with the median of each group.
# Fill missing delivery_days with the median for that city
df['delivery_days'] = (
df['delivery_days']
.fillna(df.groupby('city')['delivery_days'].transform('median'))
)
# Rows in a city with no values at all stay NaN; fall back to overall median
df['delivery_days'] = df['delivery_days'].fillna(df['delivery_days'].median())
Each comparison at alpha 0.05 has a 5% false positive chance, so checking 20 metrics will probably flag one as significant by luck. This is the multiple comparisons problem. Handle it by choosing one primary metric in advance, treating others as exploratory, and adjusting p-values with Bonferroni or Benjamini-Hochberg when several comparisons matter. With many variants, compare each to control and adjust, or use a larger sample.
A story is a sequence of sheets or dashboards, called story points, that walk an audience through an analysis step by step, like slides. Each point can keep its own filters and selections and has a caption explaining the insight. It is useful for presenting findings such as why churn rose, moving from the headline number to segments and recommendations. For everyday monitoring, a regular dashboard is better than a story.
10
How would you automate a weekly report with Python?
Pull data with SQL or APIs, clean and aggregate with Pandas, build charts and an Excel or PDF output, and schedule it with cron or Airflow with logging and alerts.
11
What is the difference between mean and median for reporting salaries?
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.
13
Your 50/50 test has 52,400 users in control and 47,600 in treatment. What does this tell you?
This is a sample ratio mismatch. A chi-square test on these counts gives a tiny p-value, so the gap is not chance. Something is filtering users unevenly, such as the treatment page crashing on some devices, a redirect losing tracking, bots in one arm or a bug in assignment. The results cannot be trusted until the cause is found. Check splits by browser, platform and day to locate it.
14
Your findings deck has 30 slides and you have 10 minutes with leadership. How do you cut it down?
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.
15
Write a Tableau table calculation for year-over-year sales growth.
// with YEAR(Order Date) in the view, Compute Using: Table (across)
(ZN(SUM([Sales])) - LOOKUP(ZN(SUM([Sales])), -1))
/ ABS(LOOKUP(ZN(SUM([Sales])), -1))
// the built-in Quick Table Calculation 'Year over Year Growth' generates similar logic
// format the field as a percentage
16
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.
17
Write Python code to bootstrap a 95% confidence interval for the difference in revenue per user between two groups.
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.
Check data pipelines first, then segment by channel, region, product and device, compare to the same period last year, and look for launches, outages or campaigns.
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.
Situation: for a college placement project, I received survey data from three forms with different column names two days before submission. Task: combine and analyse it. Action: I mapped columns to a standard list, cleaned obvious issues like duplicate responses and mixed date formats in Power Query, and noted assumptions. I focused analysis on the five questions that mattered most. Result: we submitted on time, and the faculty praised the documented cleaning steps.
24
What is the difference between concat, merge and join in pandas?
concat stacks DataFrames vertically (more rows) or side by side along an axis, for example appending monthly files with the same columns. merge combines DataFrames on key columns, like a SQL join, with how='inner', 'left', 'right' or 'outer'. join is a shortcut for merging on the index. Use concat to append and merge to look up related data. Always check row counts after a merge.
25
What is a holdout group and why do teams keep one?
A holdout group is a small share of users, often 5-10%, kept out of a feature or of all marketing campaigns for a long time. Comparing them with everyone else shows the cumulative long-term impact, which individual short tests miss, and checks whether many small wins really add up. For example, a notification holdout shows whether push messages raise monthly retention or just shift activity earlier.
26
A marketing campaign shows more sales. How do you prove it worked?
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.
28
Write a Tableau calculated field that keeps only sales from the last 30 days.
// Calculated field: Last 30 Days Flag
DATEDIFF('day', [Order Date], TODAY()) <= 29
AND [Order Date] <= TODAY()
// Put this field on Filters and keep True.
// For a sales measure instead of a filter:
// IF DATEDIFF('day', [Order Date], TODAY()) <= 29 THEN [Sales] END
29
How do you pivot rows into columns in SQL, for example sales by quarter as columns?
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
30
What is a transaction and what do the ACID properties mean?
A transaction is a group of SQL statements that succeed or fail together, such as debiting one account and crediting another. ACID: Atomicity means all or nothing; Consistency means rules and constraints stay valid; Isolation means concurrent transactions do not see each other's partial work; Durability means committed data survives crashes. Analysts mostly read data, but knowing ACID explains why reports should read committed data.
31
A measure shows correct values per row in a table visual, but the grand total looks wrong. Why, and how do you fix it?
Totals in DAX are not the sum of visible rows; the measure is recalculated in the total's filter context. A measure like a distinct count, a ratio or an IF condition can give a different result at total level. If the business wants the sum of row values, iterate explicitly, for example SUMX ( VALUES ( Product[Name] ), [My Measure] ). Confirm with stakeholders which total is correct before changing it.
32
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.
In ETL, data is extracted from sources, transformed on a separate engine and then loaded into the warehouse. In ELT, raw data is loaded first and transformed inside the warehouse using SQL, often with a tool like dbt. Cloud warehouses with cheap storage and scalable compute made ELT the common choice in 2026, because raw data stays available for reprocessing. ETL still suits cases needing masking or heavy cleansing before data lands.
34
What is a slowly changing dimension? Explain Type 1 and Type 2.
A slowly changing dimension handles attributes that change over time, like a customer's city. Type 1 overwrites the old value, so history is lost and past sales appear under the new city. Type 2 adds a new row with a new surrogate key plus valid_from, valid_to and is_current columns, so old sales stay linked to the old city. Type 2 preserves accurate history but makes the table larger and joins more careful.
35
Orders arrive in the warehouse with customer IDs that do not exist yet in the customer dimension. What do you do?
These are late-arriving dimensions. Do not drop the orders, or revenue will be understated. Common practice is to insert a placeholder customer row with the natural key and 'Unknown' attributes, link the order to it, and update that row when full details arrive. Alternatively map to a single Unknown member with key -1. Also monitor how many such rows appear daily, because a spike usually means a broken feed.
36
What is the difference between ALL, ALLEXCEPT and REMOVEFILTERS in DAX?
ALL removes filters from a table or columns and can also return all rows as a table. REMOVEFILTERS only removes filters and is clearer inside CALCULATE. ALLEXCEPT removes all filters on a table except the columns you list. A common use is share of total: DIVIDE([Sales], CALCULATE([Sales], ALL(Product))) gives each product's percentage, while ALLEXCEPT(Product, Product[Category]) gives share within its category.
37
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
Open the semantic model's refresh history in the Service to see whether refreshes failed, for example due to expired credentials, an offline gateway or a timeout. Check whether the report is connected to a different copy of the model, whether the source itself was updated, and whether incremental refresh only covers some partitions. Also confirm the schedule time zone. Finally, add a 'data as of' timestamp on the report.
df['sales'].rolling(window=7).mean() gives a 7-period moving average, useful to smooth daily noise.
Tips to crack the BNY Data Analytics interview
Learn what custody and asset servicing mean, since these are core BNY businesses and often discussed with candidates.
Revise Java or Python, SQL, OOP and DBMS concepts, and practise easy to medium coding problems.
Prepare a clear explanation of your projects, highlighting testing, documentation and teamwork, which suit regulated banking environments.
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; BNY interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the BNY Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + coding); Technical interview; 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 BNY Data Analytics interview?
This practice set leans on SQL (8), Dashboards and KPIs (5), Tableau (5), Power BI (5). Revise those first, then practise explaining one or two of your own projects end to end, because most BNY interviewers ask you to walk through real work.
Are these the exact questions asked at BNY?
No. These are curated practice questions that match the skills and difficulty usually tested in banking & finance interviews like BNY's. They are not a leaked or verified list of past questions.
Where does BNY hire Data Analytics professionals in India?
BNY has large teams in Pune, Chennai. Openings and locations change often, so check the BNY careers page and filter by city and experience.