GlobalLogic Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About GlobalLogic hiring for Data Analytics roles
GlobalLogic, a Hitachi Group company, is a digital engineering services firm working on product development, embedded software, cloud and data for clients in automotive, healthcare, media and telecom. Its India engineering centres are among its largest. It hires for software engineers, test engineers, data engineers and embedded developers.
For Data Analytics roles, this GlobalLogic practice set focuses most on Excel (6), Python for Analysis (6), Data Modelling & Warehousing (5), SQL (4). It has 14 easy, 24 medium, 2 hard questions, so you can start easy and work up to the harder ones.
Company type
IT services
Headquarters
San Jose, California, USA
Main India offices
Noida, Bengaluru, Gurugram, Hyderabad, Nagpur
GlobalLogic Data Analytics interview process
Online assessment (aptitude + coding)
Technical round 1
Technical round 2
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
Solved for GlobalLogicStart practicing
3 QUESTIONS FREESign up to unlock the full question bank and track your progress.Unlock every question and answer, then continue practising company-wise and package-wise.
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.
02
You launched a dashboard a month ago and usage logs show almost nobody opens it. What do you do?
Talk to the intended users before redesigning. Find out whether they know it exists, whether it answers their real questions, whether they trust the numbers, and whether it fits their routine. Common fixes are fewer, sharper KPIs, faster loading, a scheduled email snapshot, or embedding it in a meeting that already happens. Then measure usage again. Sometimes the honest answer is that the dashboard is not needed and can be retired.
A role-playing dimension is one physical dimension used several times by the same fact table in different roles. The classic case is the date dimension: an orders fact has order date, ship date and delivery date keys, all pointing to the same date table. In SQL you join it three times with different aliases; in Power BI you keep one active relationship and use USERELATIONSHIP or create separate date views for each role.
A bridge table resolves a many-to-many relationship between a fact and a dimension. For example, one bank account can have several holders and one customer can hold several accounts, so a bridge table stores account_key, customer_key and often an allocation weight. Without it, joining facts to customers duplicates amounts. Use weights to split balances, or report clearly that totals across customers will exceed the true total.
08
Difference between a CTE, a subquery and a temp table?
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.
09
What is the difference between extract filters and data source filters in Tableau?
Extract filters decide which rows go into the extract file at all, which reduces its size and speeds every view; filtered rows are simply not available. Data source filters apply to all worksheets using that data source, for live or extract, and are useful for security or scope, such as only the India business. Both act before context filters. Normal worksheet filters only affect one sheet.
10
What is a query execution plan and how do you read it?
An execution plan shows how the database will run a query: which indexes it uses, join methods such as nested loop or hash join, scan types and estimated rows. You get it with EXPLAIN, or EXPLAIN ANALYZE for actual timings. Look for full table scans on big tables, large gaps between estimated and actual rows, and expensive sorts. These point to missing indexes, stale statistics or non-sargable filters like functions on columns.
A p-value is the probability of seeing a result at least as extreme as yours if the null hypothesis, usually no effect, were true. A p-value of 0.03 means that if the new page truly made no difference, results this strong would appear only about 3 percent of the time by chance. Below a chosen threshold such as 0.05 we reject the null. It is not the probability that the hypothesis is true.
12
How do you combine text from several cells with a separator in Excel?
-- join first and last name with a space
=TEXTJOIN(" ", TRUE, A2, B2)
-- all products of one order in a single cell, comma separated
=TEXTJOIN(", ", TRUE, FILTER(C2:C500, A2:A500=F2))
-- older method
=A2 & " " & B2
-- TRUE in TEXTJOIN skips empty cells, which CONCAT and & do not
13
How do you handle null values in Tableau calculations?
// treat missing sales as zero
ZN(SUM([Sales]))
// replace a missing value with a default
IFNULL([Region], 'Unknown')
// test explicitly
IF ISNULL([Discount]) THEN 0 ELSE [Discount] END
// right-click a null indicator on an axis to filter or show nulls at default position
SUMIFS adds values that meet several conditions; COUNTIFS counts rows that meet several conditions.
Example: =SUMIFS(Sales, Region, "North", Month, "Jan")
COALESCE returns the first non-null value from its arguments, for example COALESCE(phone, alt_phone, 'Not given') fills missing phone numbers. NULLIF(a, b) returns NULL when a equals b, otherwise a. Its classic use is avoiding division by zero: revenue / NULLIF(orders, 0) returns NULL instead of an error when orders is zero. Together they handle most null-related reporting problems cleanly.
16
Write SQL to calculate users, conversions and conversion rate for each test variant.
SELECT
a.variant,
COUNT(DISTINCT a.user_id) AS users,
COUNT(DISTINCT o.user_id) AS converters,
ROUND(100.0 * COUNT(DISTINCT o.user_id)
/ COUNT(DISTINCT a.user_id), 2) AS conversion_pct
FROM experiment_assignments a
LEFT JOIN orders o
ON o.user_id = a.user_id
AND o.order_ts >= a.assigned_ts
WHERE a.experiment_id = 'checkout_v2'
GROUP BY a.variant;
17
Write a CREATE TABLE statement for a sales fact table linked to date, product and store dimensions.
CREATE TABLE fact_sales (
date_key INT NOT NULL REFERENCES dim_date(date_key),
product_key INT NOT NULL REFERENCES dim_product(product_key),
store_key INT NOT NULL REFERENCES dim_store(store_key),
invoice_no VARCHAR(20) NOT NULL, -- degenerate dimension
quantity INT NOT NULL,
net_amount DECIMAL(12,2) NOT NULL,
discount_amount DECIMAL(12,2) DEFAULT 0
);
18
What are statistical power and minimum detectable effect (MDE)?
Power is the probability that the test detects a real effect of a given size, commonly set at 80%. The minimum detectable effect is the smallest lift you care about and can reliably detect with your sample size. Smaller MDEs, lower baseline rates and higher power all need more users. If you can only get 10,000 users a week, you may only be able to detect large lifts, which should shape what you test.
19
A stakeholder wants 20 more visuals added to an already crowded dashboard page. How do you respond?
I would ask which decisions each new visual supports and how often they will look at it. Usually a few are essential and the rest are nice to have. I would propose keeping the main page focused on core KPIs and moving detail into drill-through pages, tooltips or a separate analysis tab. Showing a quick mock-up of both versions helps them see that the cleaner page answers questions faster.
20
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.
21
What is the LET function in Excel and why would you use it?
LET assigns names to intermediate results inside a single formula, for example =LET(sales, SUM(D2:D100), cost, SUM(E2:E100), (sales-cost)/sales). It makes long formulas readable and faster, because a repeated sub-calculation is evaluated once instead of several times. It is especially useful with dynamic arrays, where the same FILTER result might otherwise be recalculated three or four times in one nested formula.
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.
25
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.
Readers scan from the top-left, so place the most important KPIs as cards across the top, then trend charts, then breakdowns and detail tables at the bottom. Group related visuals, align them on a grid and keep consistent spacing, fonts and colours. Put filters in one consistent place. Limit a page to roughly five to eight visuals that answer related questions, and give the page a clear title stating its purpose.
Links between tables on key columns; cardinality can be one-to-many, many-to-one or many-to-many, and cross-filter direction controls how filters flow.
A z-score tells how many standard deviations a value is from the mean: z = (x minus mean) divided by standard deviation. A delivery time with z = 2.5 is unusually slow. Analysts use z-scores to flag outliers, often beyond plus or minus 3, to compare values on different scales, such as marks in different exams, and to look up probabilities when data is roughly normal.
31
A stakeholder asks for a pie chart of 12 product categories. When is a pie chart acceptable, and what would you suggest?
A pie chart works only for showing parts of a whole with very few categories, two to four, where one share clearly dominates, such as 70 percent of orders are prepaid. Avoid it when there are many slices, when values are close, or when comparing across periods, because people judge angles poorly. A sorted bar chart is almost always easier to read and compare. Never use 3D pies.
32
What is the medallion architecture (bronze, silver, gold)?
Medallion architecture organises a lakehouse into three layers. Bronze holds raw data exactly as ingested, for audit and reprocessing. Silver holds cleaned, deduplicated, typed and joined data. Gold holds business-ready tables such as star schemas and aggregates used by dashboards. Each layer is built from the previous one by repeatable jobs, so if a cleaning rule changes you can rebuild silver and gold from bronze.
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.
34
What is the difference between Goal Seek and Solver?
Goal Seek changes one input to hit a target output; Solver optimises with several variables and constraints, for example maximising profit under budget limits.
35
Tell me about a time you designed or improved a data model.
Situation: our sales reports used five separate Excel extracts and numbers never matched. Task: build one reliable model. Action: I listed the business questions, fixed the grain at invoice line, built conformed date, product and branch dimensions and moved logic into SQL views, then rebuilt the Power BI reports on top. Result: refresh time dropped from 40 to 6 minutes and month-end reconciliation queries fell sharply.
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.
38
What is the difference between dimensions and measures in Tableau?
Dimensions are qualitative fields such as region, product or date that slice data into groups and create headers. Measures are quantitative fields such as sales or profit that get aggregated, by default with SUM. Dragging a dimension to rows creates one row per member; dragging a measure creates an axis. A numeric field like customer ID should be converted to a dimension so it is not summed.
Use a clear subject that states the finding, such as Q3 repeat purchase rate fell 4 points, mainly in Tier 2 cities. Open with one or two sentences of the key insight and the recommendation. Follow with three bullet-style lines of supporting evidence with numbers, then any caveats about data, and a clear next step or decision needed with a date. Attach or link the detailed workings instead of pasting large tables.
Tips to crack the GlobalLogic Data Analytics interview
Revise OOP, data structures and one programming language in depth, as GlobalLogic interviews focus on engineering fundamentals.
Learn about its Hitachi Group ownership and work in automotive and embedded software if you have that interest.
Prepare to discuss testing practices, including unit tests and automation, for both developer and QA roles.
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; GlobalLogic interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the GlobalLogic Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + coding); Technical round 1; Technical round 2; 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 GlobalLogic Data Analytics interview?
This practice set leans on Excel (6), Python for Analysis (6), Data Modelling & Warehousing (5), SQL (4). Revise those first, then practise explaining one or two of your own projects end to end, because most GlobalLogic interviewers ask you to walk through real work.
Are these the exact questions asked at GlobalLogic?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like GlobalLogic's. They are not a leaked or verified list of past questions.
Where does GlobalLogic hire Data Analytics professionals in India?
GlobalLogic has large teams in Noida, Bengaluru, Gurugram, Hyderabad, Nagpur. Openings and locations change often, so check the GlobalLogic careers page and filter by city and experience.