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Data Analytics Interview Questions in Hyderabad

36 practice questions with answers for Data Analytics interviews in Hyderabad.

Data Analytics jobs in Hyderabad

Hyderabad has large engineering and capability centres of global technology, banking and pharma companies around HITEC City and the Financial District. Many roles are with global capability centres that work with teams in the US and Europe.

Below are 36 questions that match the mix of service, product and domain employers hiring data analysts in Hyderabad.

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01

What is the grain of a fact table and why should you decide it first?

Data Modelling & WarehousingMedium
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The grain is the exact meaning of one row in a fact table, such as one order line, one daily store total or one monthly account balance. You decide it first because it controls which dimensions can join, which measures are valid and how totals behave. Mixing grains in one table, for example order lines and order-level shipping charges, causes double counting. Always state the grain in one sentence before adding columns.
02

What is a CTE and why use it?

SQLMedium
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A Common Table Expression is a named temporary result defined with WITH that makes complex queries readable and allows recursion.
03

What is a data mart?

Data Modelling & WarehousingEasy
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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.
04

What is a composite model in Power BI?

Power BIHard
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A composite model mixes storage modes in one dataset: some tables in Import, some in DirectQuery, and some in Dual. For example, a huge transaction table stays in DirectQuery for freshness while small dimension tables are imported for speed. Aggregation tables can be imported so most visuals hit memory, with DirectQuery used only for detailed drill-down. The trade-off is more complexity and relationship rules to manage.
05

How would you find customers who never placed an order?

SQLMedium
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LEFT JOIN customers to orders and keep rows where order_id IS NULL, or use NOT EXISTS, which also handles NULLs safely.
06

What is Tableau Prep used for?

TableauEasy
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Tableau Prep is a visual tool for cleaning and shaping data before analysis. You build a flow of steps: connect inputs, clean fields, split columns, group similar values, pivot, join, union and aggregate, and see the data change at every step. The output can be an extract or published data source that dashboards use. It suits analysts who need repeatable preparation without writing SQL or Python.
07

What does apply do in pandas and why can it be slow?

Python for AnalysisMedium
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apply runs a Python function on each row or column of a DataFrame, or each value of a Series. It is flexible but slow on large data because it loops in Python rather than using optimised C code. For example, df['total'] = df['qty'] * df['price'] is far faster than an apply with a lambda. Prefer vectorised operations, np.where, np.select, map with a dictionary, or string accessor methods first.
08

When would you use a one-tailed test instead of a two-tailed test?

A/B Testing & ExperimentsEasy
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A two-tailed test checks for a difference in either direction and is the safer default for A/B tests. A one-tailed test checks only one direction, such as treatment being better, and needs fewer users. Use it only if you decided beforehand that you would act the same way whether the change has no effect or a negative effect. Otherwise you may miss harm, which is why most teams stay two-tailed.
09

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?

Data Modelling & WarehousingMedium
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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.
10

What is a date table in Power BI and why should you mark it as one?

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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.
11

Write a Tableau calculation to find each customer's first purchase date and flag new versus repeat orders.

TableauMedium
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// First Purchase Date { FIXED [Customer ID] : MIN([Order Date]) } // Order Type IF [Order Date] = [First Purchase Date] THEN 'New' ELSE 'Repeat' END // Cohort Month for retention analysis DATETRUNC('month', [First Purchase Date])
12

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.
13

How do you handle outliers in Pandas?

Python for AnalysisMedium
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Detect with IQR or z-score, then cap with clip, remove, transform with log, or keep and flag them depending on whether they are errors or real events.
14

What do the match_mode and search_mode arguments of XLOOKUP control?

ExcelMedium
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match_mode decides how a value is matched: 0 exact (default), -1 exact or next smaller, 1 exact or next larger, and 2 wildcard. search_mode decides direction: 1 searches first to last, -1 searches last to first, and 2 or -2 use binary search on sorted data. Searching last to first is a quick way to fetch the latest entry for a customer without sorting, and next-smaller matching handles tax slabs or commission bands.
15

What is a star schema?

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A central fact table (sales, numeric events) linked to dimension tables (date, product, customer). It keeps Power BI models simple, fast and correct when filtering.
16

What is an enterprise bus matrix?

Data Modelling & WarehousingHard
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A bus matrix is a planning grid with business processes as rows, such as orders, shipments and returns, and conformed dimensions as columns, such as date, customer, product and warehouse. A tick shows which dimensions each process uses. It helps prioritise which fact tables to build, shows which dimensions must be shared and conformed first, and gives leaders a one-page roadmap of the warehouse without technical detail.
17

Write pandas code to standardise inconsistent city names such as 'delhi ', 'New Delhi' and 'DELHI'.

Data CleaningMedium
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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())
18

Tell me about a time you automated a manual Excel task.

ExcelMedium
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Situation: our team spent about four hours every Monday copying sales exports from five branches into one summary sheet. Task: I was asked to cut that time. Action: I built a Power Query that read every file in a shared folder, cleaned headers and appended them, then a PivotTable and a few formulas produced the summary on refresh. Result: the report took ten minutes, copy-paste errors stopped, and two other teams reused the template.
19

Write pandas code to calculate monthly revenue from an orders DataFrame.

Python for AnalysisMedium
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import pandas as pd orders = pd.read_csv('orders.csv', parse_dates=['order_date']) orders['revenue'] = orders['qty'] * orders['unit_price'] monthly = ( orders .groupby(orders['order_date'].dt.to_period('M'))['revenue'] .sum() .reset_index(name='revenue') ) print(monthly)
20

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.
21

Difference between a CTE, a subquery and a temp table?

SQLMedium
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A subquery is inline; a CTE is a named, readable block for one statement; a temp table is physically stored for the session and can be indexed and reused.
22

Pin codes in your CSV lost their leading zeros after opening in Excel. Why and how do you prevent it?

Data CleaningEasy
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Excel guessed the column was a number and dropped leading zeros; the same happens with long IDs turning into scientific notation. Identifiers that are not used in arithmetic should be stored as text. Import through Data, From Text/CSV or Power Query and set the column type to Text, or read it in pandas with dtype={'pincode': str}. To repair, use =TEXT(A2, "000000") when the original length is known.
23

Write SQL to find the number of days between each customer's consecutive orders and their average gap.

SQLMedium
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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
24

How would you present the same churn analysis to the CEO and to the customer support team?

Data StorytellingMedium
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For the CEO, lead with the headline and money: churn rose 2 points, costing about 3 crore annually, mainly from one segment, with two recommended actions and their expected impact, all on one or two slides. For the support team, go deeper into drivers they control: ticket types, resolution times and specific customer pain points, with operational next steps and how progress will be tracked. Same data, different level and call to action.
25

Tell me about a time you found a mistake in a report after it had already been shared.

Data StorytellingMedium
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Situation: a day after sending a weekly report, I noticed one region's numbers included test orders. Task: correct it before decisions were made. Action: I quantified the impact, a 3 percent overstatement, and emailed the same audience with the corrected figures, a short explanation and the fix I had added to prevent repeats. Result: my manager appreciated the quick ownership, and the test-order filter became part of our shared dataset.
26

What is the difference between WHERE and HAVING?

SQLEasy
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WHERE filters rows before grouping. HAVING filters groups after GROUP BY and can use aggregates like SUM() or COUNT().
27

What is a window function?

SQLMedium
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A function that calculates over a set of rows related to the current row without collapsing them, such as ROW_NUMBER, RANK, LAG or SUM() OVER (PARTITION BY ...).
28

Average delivery time improved by 4 minutes, but customer complaints about late deliveries went up. How do you investigate?

Statistics and Case StudiesMedium
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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.
29

What steps would you apply in Power Query to clean a messy system export?

Data CleaningMedium
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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.
30

What does CASE WHEN do?

SQLMedium
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It adds conditional logic inside queries, for example bucketing ages or flagging values, returning different results per condition.
31

Your FILTER formula shows #SPILL! instead of results. Why, and how do you fix it?

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Dynamic array formulas such as FILTER, UNIQUE and SORT return several values that spill into neighbouring cells. #SPILL! appears when that spill range is blocked by existing data, merged cells, or when the formula sits inside an Excel Table, which does not allow spilling. Fix it by clearing the blocking cells, unmerging, or moving the formula outside the Table. Select the error cell to see the dashed border of the range it needs.
32

Write SQL to flag rows that fail basic validation rules in a customer table.

Data CleaningMedium
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SELECT customer_id, CASE WHEN email IS NULL OR email NOT LIKE '%_@_%._%' THEN 1 ELSE 0 END AS bad_email, CASE WHEN LENGTH(pincode) <> 6 THEN 1 ELSE 0 END AS bad_pincode, CASE WHEN signup_date > CURRENT_DATE THEN 1 ELSE 0 END AS future_signup, CASE WHEN age NOT BETWEEN 18 AND 100 THEN 1 ELSE 0 END AS bad_age FROM customers; -- sum each flag to report the error rate per rule
33

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.
34

What is a correlated subquery?

SQLMedium
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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).
35

What is a confidence interval?

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A confidence interval gives a range of plausible values for a population parameter based on a sample. A 95 percent interval for average order value of 820 to 880 rupees means that if we repeated the sampling many times, about 95 percent of such intervals would contain the true average. Wider intervals mean more uncertainty; larger samples narrow them. Reporting the interval is more honest than reporting one number.
36

What is Power Pivot and the Excel Data Model?

ExcelMedium
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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.

Companies with offices in Hyderabad (56)

IT services

TCS

Data Analytics interview questions

  • 40 questions
  • 4 rounds

Mumbai, Chennai, Pune +2

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IT services

Infosys

Data Analytics interview questions

  • 40 questions
  • 4 rounds

Bengaluru, Mysuru, Pune +2

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IT services

Wipro

Data Analytics interview questions

  • 40 questions
  • 4 rounds

Bengaluru, Hyderabad, Pune +2

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Consulting

Accenture

Data Analytics interview questions

  • 40 questions
  • 4 rounds

Bengaluru, Hyderabad, Pune +2

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IT services

Cognizant

Data Analytics interview questions

  • 40 questions
  • 4 rounds

Chennai, Bengaluru, Pune +2

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IT services

Capgemini

Data Analytics interview questions

  • 40 questions
  • 4 rounds

Mumbai, Bengaluru, Pune +2

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Consulting

Deloitte

Data Analytics interview questions

  • 40 questions
  • 4 rounds

Hyderabad, Bengaluru, Mumbai +2

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Product

Amazon

Data Analytics interview questions

  • 40 questions
  • 5 rounds

Bengaluru, Hyderabad, Chennai +2

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Product

Google

Data Analytics interview questions

  • 40 questions
  • 5 rounds

Bengaluru, Hyderabad, Gurugram +2

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Product

Meta

Data Analytics interview questions

  • 40 questions
  • 5 rounds

Gurugram, Bengaluru, Hyderabad +1

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Product

Microsoft

Data Analytics interview questions

  • 40 questions
  • 5 rounds

Hyderabad, Bengaluru, Noida +2

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Hardware & semiconductors

Apple

Data Analytics interview questions

  • 40 questions
  • 5 rounds

Hyderabad, Bengaluru, Mumbai +1

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Product

Oracle

Data Analytics interview questions

  • 40 questions
  • 5 rounds

Bengaluru, Hyderabad, Noida +2

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Product

Salesforce

Data Analytics interview questions

  • 40 questions
  • 5 rounds

Hyderabad, Bengaluru, Mumbai +1

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Product

ServiceNow

Data Analytics interview questions

  • 40 questions
  • 5 rounds

Hyderabad, Bengaluru

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Product

Uber

Data Analytics interview questions

  • 40 questions
  • 5 rounds

Bengaluru, Hyderabad, Gurugram

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E-commerce & consumer tech

Flipkart

Data Analytics interview questions

  • 40 questions
  • 5 rounds

Bengaluru, Hyderabad

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E-commerce & consumer tech

Swiggy

Data Analytics interview questions

  • 40 questions
  • 4 rounds

Bengaluru, Hyderabad

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Fintech

PayPal

Data Analytics interview questions

  • 40 questions
  • 5 rounds

Chennai, Bengaluru, Hyderabad

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Banking & finance

Goldman Sachs

Data Analytics interview questions

  • 40 questions
  • 5 rounds

Bengaluru, Hyderabad, Mumbai

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Banking & finance

JP Morgan Chase

Data Analytics interview questions

  • 40 questions
  • 5 rounds

Mumbai, Bengaluru, Hyderabad

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Banking & finance

HSBC

Data Analytics interview questions

  • 40 questions
  • 4 rounds

Hyderabad, Pune, Bengaluru

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Fintech

DE Shaw

Data Analytics interview questions

  • 40 questions
  • 5 rounds

Hyderabad, Bengaluru, Gurugram

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IT services

HCLTech

Data Analytics interview questions

  • 40 questions
  • 4 rounds

Noida, Chennai, Bengaluru +2

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+ 32 more companies — see all 155

Frequently asked questions

Which companies hire Data Analytics professionals in Hyderabad?

Companies with large offices in Hyderabad include TCS, Infosys, Wipro, Accenture, Cognizant, Capgemini, Deloitte, Amazon, Google, Meta. Use their company pages below to practise employer-specific sets.

Are Data Analytics interviews in Hyderabad different from other cities?

The questions depend more on the company than the city. Hyderabad's mix of employers decides the style: Many roles are with global capability centres that work with teams in the US and Europe

How do I find Data Analytics jobs in Hyderabad?

Shortlist companies with Hyderabad offices, keep your CV focused on Data Analytics projects, apply on company career pages and job portals with the Hyderabad filter, and practise the questions on this page before each interview.

Other cities

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