Airtel Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Airtel hiring for Data Analytics roles
Bharti Airtel is an Indian telecom company providing mobile, broadband, DTH, enterprise connectivity and digital services such as Airtel Thanks and Airtel Payments Bank. Its digital and analytics teams work on network data, customer analytics and app development. It hires for software engineers, testers, network engineers and data professionals.
For Data Analytics roles, this Airtel practice set focuses most on Data Cleaning (7), Statistics and Case Studies (6), Data Storytelling (4), Excel (4). It has 13 easy, 24 medium, 3 hard questions, so you can start easy and work up to the harder ones.
Company type
Telecom
Headquarters
New Delhi, India
Main India offices
Gurugram, Bengaluru, Mumbai, Pune
Airtel Data Analytics interview process
Online assessment (aptitude + technical)
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
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.
02
What is the difference between sets and groups in Tableau?
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.
03
How does the FILTER function work in Excel? Write a formula to list all North region orders.
-- 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"))
04
Write pandas code to find the top 2 products by revenue in each region.
Two variables can move together because of a third factor, reverse causation or pure chance. Example: stores with more staff have higher sales, but busy locations get more staff because they already sell more, so hiring alone may not raise sales. Ice-cream sales and AC sales both rise in summer because of temperature. To claim causation you need experiments, natural experiments or careful control of confounders.
State assumptions clearly. Of 1 crore people, assume 70 percent drink tea, so 70 lakh. Say half of their cups are bought outside, at an average of one bought cup per drinker every day for that half, giving about 35 lakh purchased cups, with more at offices and stations. So roughly 30 to 40 lakh cups. Interviewers judge the structure, segmentation and sanity check more than the exact number.
You assume no effect (null), collect evidence and decide whether the data is surprising enough to reject that assumption at a chosen significance level.
09
What are additive, semi-additive and non-additive facts?
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.
10
Write a DAX measure for year-over-year sales growth percentage.
Sales LY =
CALCULATE([Total Sales], SAMEPERIODLASTYEAR('Date'[Date]))
Sales YoY % =
VAR cur = [Total Sales]
VAR prev = [Sales LY]
RETURN
DIVIDE(cur - prev, prev)
-- needs a marked date table; format the measure as a percentage
A correlated subquery refers to a column from the outer query, so it is logically evaluated once per outer row. Example: find employees earning above their department average with WHERE salary > (SELECT AVG(salary) FROM emp e2 WHERE e2.dept_id = e1.dept_id). It is readable but can be slow on large tables. The same result is often faster with a window function such as AVG(salary) OVER (PARTITION BY dept_id).
It automatically formats cells based on rules such as colour scales, data bars or highlighting values above a target, making patterns visible at a glance.
Table calculations are computed on the aggregated results already in the view, not on the underlying rows. Examples are running total, percent of total, difference from previous, rank and moving average. Their result depends on the direction and partition you set with Compute Using, for example table across or by specific dimensions. They are quick to add from Quick Table Calculation, but incorrect Compute Using settings are a frequent cause of wrong numbers.
14
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.
A data mart is a subset of the warehouse built for one department or subject, such as sales, finance or HR. It contains the facts and dimensions that team needs, often pre-aggregated and with business-friendly names. Marts make reporting faster and simpler for that team. Ideally they are built from the central warehouse using conformed dimensions, so the finance mart and the sales mart still agree on revenue.
16
Organic search traffic fell 40% overnight. How do you investigate?
First check whether it is a tracking problem: did the analytics tag break on some templates, did consent settings change, does server log or backend order data show the same fall? If the drop is real, check the search console for impressions and rankings, recent site releases such as changed URLs, missing redirects, noindex tags or robots.txt changes, and whether a search algorithm update or seasonal pattern matches. Then break the drop down by page group.
17
Your Power BI dataset with five years of transactions takes two hours to refresh. How would you fix it?
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.
18
Write pandas code to trim whitespace and lower-case every text column in a DataFrame.
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.
21
What is the logical order in which SQL executes a SELECT query?
Although written as SELECT first, a query is logically processed as FROM and JOIN, then WHERE, GROUP BY, HAVING, SELECT, DISTINCT, ORDER BY and finally LIMIT. This explains common errors: you cannot use a SELECT alias in WHERE because SELECT runs later, but you can use it in ORDER BY. It also explains why aggregates belong in HAVING, not WHERE, and why window functions cannot be filtered in the same query level.
22
What is the difference between COUNT, COUNTA and COUNTIF?
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.
24
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.
25
What are strategic, operational and analytical dashboards?
Strategic dashboards serve leadership with a few high-level KPIs and trends, updated daily to monthly. Operational dashboards monitor live or near-live activity for teams on the ground, such as orders pending dispatch or call queue length, often with alerts. Analytical dashboards let analysts and managers explore data with filters and drill-downs to find causes. Knowing which type you are building decides the level of detail, refresh rate and design.
26
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.
Dimensional modelling is a way of designing analytical tables around business processes. Numeric events such as sales or payments go into fact tables, and descriptive context such as customer, product, store and date goes into dimension tables. The design is easy for business users to understand and fast for BI tools to aggregate. For example, a retail model might have one sales fact joined to date, product, store and customer dimensions.
28
What is the difference between population and sample?
Data storytelling is communicating insights by combining three things: data, the evidence; visuals, charts that make patterns obvious; and narrative, the context and so-what that explains why it matters and what to do. A table of numbers informs, but a story persuades someone to act. For an analyst, it means leading with the key finding, showing just enough evidence, and ending with a clear recommendation.
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.
31
How would you investigate a sudden 20% drop in daily sales?
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 simple structure is context, conflict, resolution. Context sets up what the audience already knows, such as our target was 10 percent growth. Conflict shows the surprising finding or problem, such as growth is only 3 percent because repeat buyers dropped. Resolution gives the recommendation and expected impact. Another option is situation, complication, question, answer. Either way, the audience should know the main message within the first minute.
33
Marketing wants to A/B test two email subject lines on a list of 2,000 subscribers. What do you advise?
With 1,000 per group and a typical click rate of 3%, only a very large difference, roughly 70% relative or more, would be detectable, so a small real lift would look like noise. I would suggest testing on a larger send, pooling several campaigns, or using open-to-click rate with a bigger effect. If they still run it, report the confidence interval and treat the result as directional.
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.
36
What is referential integrity and how do orphan records appear in analytics data?
Referential integrity means every foreign key value points to an existing row in the parent table, for example every order's customer_id exists in customers. Orphan records break this rule. They appear when source systems load tables at different times, records are deleted without cascading, or IDs are formatted differently across systems. Detect them with a LEFT JOIN where the parent key is NULL, and decide whether to fix, map or flag them.
37
Two tables have a many-to-many relationship and your report totals look inflated. What do you do?
Inflated totals usually mean rows are being double counted through the many-to-many path. I would first check whether a proper dimension table can sit between them, turning it into two one-to-many relationships, for example a bridge table of unique customer IDs. I would set single filter direction, test totals against the source with a simple SQL query, and use measures with DISTINCTCOUNT where needed. Many-to-many should be a deliberate choice, not a default.
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.
39
What is the difference between OLTP and OLAP systems?
OLTP systems run day-to-day operations, such as billing or order entry. They handle many small inserts and updates, use normalised tables and need fast single-row access. OLAP systems support analysis, scanning millions of rows to aggregate sales by month or region. They use denormalised star schemas, columnar storage and are mostly read-heavy. Analysts should query OLAP copies so heavy reports do not slow down the live application.
40
An inner join between orders and customers returns fewer orders than the orders table has. What could be wrong?
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.
Tips to crack the Airtel Data Analytics interview
Learn telecom basics like ARPU, churn, 4G and 5G networks, and broadband rollout for case discussions.
Prepare analytics cases on reducing customer churn or improving network quality using data.
Read about Airtel's digital products like Airtel Thanks and Xstream, and its enterprise business Airtel Business.
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; Airtel interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Airtel Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + technical); 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 Airtel Data Analytics interview?
This practice set leans on Data Cleaning (7), Statistics and Case Studies (6), Data Storytelling (4), Excel (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Airtel interviewers ask you to walk through real work.
Are these the exact questions asked at Airtel?
No. These are curated practice questions that match the skills and difficulty usually tested in telecom interviews like Airtel's. They are not a leaked or verified list of past questions.
Where does Airtel hire Data Analytics professionals in India?
Airtel has large teams in Gurugram, Bengaluru, Mumbai, Pune. Openings and locations change often, so check the Airtel careers page and filter by city and experience.