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Data Analytics Interview Questions for E-commerce & consumer tech Companies

40 questions weighted to the interview style of e-commerce & consumer tech companies — 3 easy, 31 medium, 6 hard.

How e-commerce & consumer tech companies interview for Data Analytics

E-commerce and consumer-internet companies work at large scale with fast release cycles. Interviews focus on practical skill, metrics such as conversion and retention, scenario questions from real product problems, and how you work under tight timelines.

This set focuses on Power BI (7), Dashboards and KPIs (6), Python for Analysis (6), Tableau (5).

Hiring manager round HR round Technical or case round Technical round 1 Online assessment (coding or SQL)

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How do you handle errors like #N/A and #DIV/0!?

ExcelMedium

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Fix the root cause when possible, or wrap formulas with IFERROR or IFNA to return a clear fallback value.

What is a story in Tableau?

TableauEasy

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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.

What is the difference between sets and groups in Tableau?

TableauMedium

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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.

Which KPIs would you track for an e-commerce app?

Dashboards and KPIsMedium

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DAU/MAU, conversion rate, average order value, cart abandonment, retention/churn, CAC, LTV, return rate and NPS.

How do you calculate month-over-month growth?

Dashboards and KPIsMedium

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Aggregate revenue by month, use LAG(revenue) OVER (ORDER BY month), then (revenue - prev) / prev x 100.

Write a Tableau table calculation for year-over-year sales growth.

TableauMedium

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// 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

Import mode vs DirectQuery?

Power BIMedium

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Import loads data into memory for fast visuals; DirectQuery queries the source live, giving fresh data but slower performance and some DAX limits.

A measure shows correct values per row in a table visual, but the grand total looks wrong. Why, and how do you fix it?

Power BIMedium

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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.

What are leading and lagging indicators? Give examples.

Dashboards and KPIsMedium

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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.

How do you calculate a rolling average?

SQLMedium

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df['sales'].rolling(window=7).mean() gives a 7-period moving average, useful to smooth daily noise.

How do you find duplicate rows in a table?

SQLMedium

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SELECT col, COUNT(*) FROM t GROUP BY col HAVING COUNT(*) > 1; use ROW_NUMBER() partitioned by the key to delete extras.

How do you merge two DataFrames on different column names?

Python for AnalysisMedium

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pd.merge(df1, df2, left_on='cust_id', right_on='id', how='left') joins on differently named keys.

Write SQL to calculate daily active users and the DAU/MAU stickiness ratio.

Dashboards and KPIsHard

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WITH dau AS ( SELECT CAST(event_ts AS DATE) AS day, COUNT(DISTINCT user_id) AS dau FROM events GROUP BY CAST(event_ts AS DATE) ) SELECT d.day, d.dau, (SELECT COUNT(DISTINCT e.user_id) FROM events e WHERE CAST(e.event_ts AS DATE) BETWEEN d.day - INTERVAL '29 days' AND d.day) AS mau, ROUND(100.0 * d.dau / (SELECT COUNT(DISTINCT e.user_id) FROM events e WHERE CAST(e.event_ts AS DATE) BETWEEN d.day - INTERVAL '29 days' AND d.day), 1) AS stickiness_pct FROM dau d ORDER BY d.day;

What is vectorisation and why does it matter?

Python for AnalysisMedium

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Applying operations to whole arrays at once instead of Python loops, which is far faster because it runs in optimised compiled code.

Write a DAX measure for cumulative sales from the start of the data to the selected date.

Power BIMedium

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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])

Your analysis shows that a flagship initiative championed by leadership is not working. How do you present it?

Data StorytellingHard

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Prepare carefully and stay factual. Share the finding early with the initiative owner privately so they are not surprised in a big meeting. Lead with the question asked, the method and the evidence, acknowledge limitations and what is working, and avoid blaming anyone. Offer options, such as fixing a specific step, narrowing the scope or stopping, with estimated impact. Bad news delivered with solutions is received far better.

Your sales data contains negative quantities. What do you do with them?

Data CleaningMedium

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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.

How do you handle large CSV files that do not fit in memory in Python?

Python for AnalysisMedium

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Read in chunks with chunksize, load only needed columns, set efficient dtypes or switch to Dask, Polars or a database.

How do you let users choose Top N customers with a parameter in Tableau?

TableauMedium

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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.

What are the main dimensions of data quality?

Data CleaningMedium

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Common dimensions are accuracy, values reflect reality; completeness, required fields are present; consistency, the same fact matches across systems; validity, values follow formats and rules such as a 6-digit pin code; uniqueness, no unwanted duplicates; and timeliness, data is fresh enough for its use. Measuring these with simple checks, like percent missing or duplicate rate, turns vague complaints about bad data into fixable issues.

Write pandas code to compute month-on-month percentage change and cumulative sales for each store.

Python for AnalysisMedium

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df = df.sort_values(['store', 'month']) df['mom_pct'] = ( df.groupby('store')['sales'].pct_change() * 100 ).round(1) df['cum_sales'] = df.groupby('store')['sales'].cumsum() # first month of each store has NaN for mom_pct, which is expected

Users say a Power BI report still shows yesterday morning's data, although a refresh is scheduled every few hours. What do you check?

Power BIMedium

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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.

Email open rates suddenly jumped from 22% to 45% without any campaign change. Should you celebrate?

Web & Marketing AnalyticsMedium

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No. Privacy features in some mail apps pre-load email images, which fires the open tracking pixel even if the person never reads the email, so open rates get inflated. Check opens by email client to confirm. Move reporting towards clicks, click-to-send rate, conversions and unsubscribes, which reflect real behaviour, and avoid using opens alone to pick winning subject lines or to define engaged subscribers.

Traffic suddenly doubled from one city, with near-zero engagement time and no conversions. What is happening and what do you do?

Web & Marketing AnalyticsMedium

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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.

Your Power BI dataset with five years of transactions takes two hours to refresh. How would you fix it?

Power BIHard

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Incremental refresh partitions a large table by date so that each refresh reloads only recent data, for example the last 7 days, while older partitions stay untouched. You create RangeStart and RangeEnd parameters, filter the table with them in Power Query, and set a policy such as store 5 years, refresh 7 days. Refresh becomes faster and lighter on the source. Query folding must work for the filter to be pushed to the database.

How do you improve Power BI report performance?

Power BIMedium

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Use a star schema, remove unused columns, prefer measures, reduce visuals per page, limit high-cardinality fields and use aggregations or incremental refresh.

How do you make a dashboard accessible, for example for colour-blind users?

Dashboards and KPIsMedium

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Avoid red-green as the only difference; use colour-blind-safe palettes such as blue and orange, and add icons, labels or patterns so meaning never depends on colour alone. Ensure enough contrast between text and background, use readable font sizes, and add alt text to visuals. Keep a logical tab order for keyboard users, and offer a table view of chart data. Accessible dashboards are usually clearer for everyone.

Tell me about a time your analysis was challenged in a meeting.

Data StorytellingMedium

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Situation: while presenting regional sales, a manager said my numbers were wrong because his team's tracker showed more. Task: handle it without derailing the meeting. Action: I acknowledged the concern, explained my source and definition briefly, and offered to reconcile after the meeting. That afternoon we found his tracker counted orders before cancellations. Result: we agreed on net orders as the standard, and he later asked me to rebuild his tracker.

Write Python code to test whether conversion differs between control and treatment.

A/B Testing & ExperimentsMedium

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from statsmodels.stats.proportion import proportions_ztest, proportion_confint conversions = [1180, 1295] # control, treatment visitors = [30000, 30100] z, p = proportions_ztest(conversions, visitors) print(f'z = {z:.2f}, p = {p:.4f}') for c, n in zip(conversions, visitors): low, high = proportion_confint(c, n, method='wilson') print(f'rate {c/n:.3%} 95% CI {low:.3%} to {high:.3%}')

An inner join between orders and customers returns fewer orders than the orders table has. What could be wrong?

Data CleaningMedium

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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.

Write SQL to find users who logged in on at least 3 consecutive days.

SQLHard

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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

Your scheduled Python report failed silently and nobody noticed for a week. How do you make it reliable?

Python for AnalysisMedium

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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.

How do you create a dynamic dashboard in Excel?

ExcelMedium

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Build PivotTables from a clean table, add PivotCharts and slicers, link KPIs with formulas, and refresh from Power Query so everything updates together.

Analytics shows 1,820 purchases last month but the order database shows 2,150. Which is right, and how do you explain the gap?

Web & Marketing AnalyticsMedium

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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.

What is the difference between relationships, joins and data blending in Tableau?

TableauMedium

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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.

SUMIFS vs COUNTIFS?

ExcelEasy

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SUMIFS adds values that meet several conditions; COUNTIFS counts rows that meet several conditions. Example: =SUMIFS(Sales, Region, "North", Month, "Jan")

Your pandas script takes 40 minutes on 10 million rows. How do you speed it up?

Python for AnalysisHard

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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.

What is Simpson's paradox? Give an example.

Statistics and Case StudiesHard

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Simpson's paradox happens when a trend in several groups reverses when the groups are combined, because group sizes differ. Example: a new sales process has higher conversion in both small and large stores, yet overall conversion looks lower because it was mostly rolled out to large stores, which convert worse in general. The lesson is to check segments and confounding variables before concluding from aggregate numbers.

DAX: calculated column vs measure?

Power BIMedium

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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.

What are vanity metrics?

Dashboards and KPIsEasy

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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.