Nykaa Software Engineer & Analytics — Hiring Structure, Syllabus & 8-Week Prep Plan 2026
Nykaa hires for Software Engineering and Analytics through specialized tracks. Software roles focus on DSA, low-level design (catalogue, cart, inventory) and system scalability. Analytics roles are SQL-intensive, covering window functions, cohort analysis, e-commerce metrics (GMV, AOV, returns) and business cases. Prepare for the beauty-tech giant using the roadmap below.
Software: 2 medium problems in 90 min (DSA + Design) · Analytics: SQL-heavy assessment (Window functions, metrics) + business cases.
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Exam Pattern
Software: 2 medium problems in 90 min (DSA + Design) · Analytics: SQL-heavy assessment (Window functions, metrics) + business cases.
| Section | Questions | Time | Type | Difficulty |
|---|---|---|---|---|
| Software Engineer · Online assessment | 2 problems | 90 min | Easy-to-medium/medium DSA | Medium |
| Software Engineer · DSA interview | 1–2 problems | 45–60 min | Coding and complexity | Medium–Hard |
| Software Engineer · Technical / design round | Varies | 45–60 min | LLD, APIs, product scenarios | Hard |
| Software Engineer · Hiring manager | Varies | 30–45 min | Projects & practical thinking | Medium |
| Analytics · Online assessment | Mixed | Timed | SQL, aptitude and data interpretation | Medium |
| Analytics · Technical / data round | Mixed | 45–60 min | SQL, Python/pandas and statistics | Medium–Hard |
| Analytics · Business case | 1–2 cases | 45–60 min | Revenue, returns, inventory, marketing | Hard |
Section-Wise Syllabus
Everything that appears in the assessment, grouped by section.
1. Software Engineer — DSA & Design
Arrays & strings
- • Prefix sums
- • Difference arrays
- • Two pointers
- • Sliding window
- • Hashing
- • Sorting-based optimisation
- • Subarrays & subsequences
- • Palindromes
- • Anagrams
- • Minimum window
- • Duplicate product detection
Linked lists & stacks
- • Reverse linked list
- • Reverse in groups of k
- • Detect a cycle
- • Merge sorted lists
- • Clone random-pointer list
- • Balanced parentheses
- • Min stack
- • Next greater element
- • Sliding-window maximum
Trees & graphs
- • Height & diameter
- • Lowest common ancestor
- • Validate BST
- • Serialize & deserialize
- • BFS & DFS
- • Cycle detection
- • Topological sorting
- • Dijkstra
- • Category-hierarchy traversal
Dynamic programming
- • House robber
- • Coin change
- • Knapsack
- • Subset sum
- • LIS & LCS
- • Edit distance
- • Stock buy-sell
- • Partition DP
LLD & system design
- • SOLID principles
- • Factory & Strategy patterns
- • Observer & State patterns
- • Dependency injection
- • Shopping cart system
- • Product catalogue
- • Inventory reservation
- • Discount/coupon engine
- • Notification service
2. Analytics — SQL, Python & statistics
SQL mastery
- • Joins & Aggregation
- • Subqueries & CTEs
- • Window functions (ROW_NUMBER, RANK, NTILE)
- • LAG & LEAD
- • Date & time functions
- • Conditional aggregation
- • Cohort & Funnel analysis
- • Retention & Running totals
Python & pandas
- • Lists, dicts, sets
- • NumPy
- • pandas groupby
- • merge & join
- • pivot_table
- • Filtering & Sorting
- • Missing-value handling
- • Date conversion
- • Aggregation basics
Statistics
- • Mean, median, mode
- • Variance & SD
- • Percentiles
- • Probability
- • A/B testing
- • P-values & Statistical power
- • Type I & Type II errors
- • Correlation vs Causation
- • Regression basics
E-commerce metrics
- • GMV & Revenue
- • Add-to-cart rate
- • Checkout conversion
- • Customer lifetime value (LTV)
- • CAC
- • Return & Cancellation rates
- • Stockout rate
- • Inventory turnover
- • Shade availability (Beauty specific)
Most Asked Topics
Highest-frequency areas across recent test papers.
Category hierarchy traversal
DFS/BFS problems involving product trees or multi-level brand groupings.
SQL window functions
ROW_NUMBER, RANK, and NTILE appear frequently in the Analytics online assessment.
Inventory reservation design
LLD round focus on temporary locks, expiry, concurrency and out-of-stock handling.
Trending products
Sliding window with frequency maps to identify bestsellers within time K.
Return rate calculations
Conditional aggregation to identify sellers or categories with high returns (>6%).
Product recommendation
Design discussions covering embeddings, candidate generation, and ranking for beauty shoppers.
Free Resources
Curated practice links per section.
Software Engineering
Analytics & Data
50+ DSA Practice Problems
Solve in order — arrays and strings first, then sorting and recursion.
Arrays & Strings
- Longest substring without repetition
- Maximum subarray sum
- Smallest missing positive integer
- Product of array except self
- Merge intervals
- Longest consecutive sequence
Lists & Stacks
- Reverse in groups of k
- Detect cycle
- Merge k sorted lists
- Min stack
- Next greater element
- Largest rectangle in histogram
Trees & Graphs
- Lowest common ancestor
- Serialize & deserialize tree
- Number of islands
- Course schedule (Topological sort)
- Dijkstra's shortest path
DP
- Edit distance
- Coin change
- Longest increasing subsequence
- Partition equal subset sum
- Stock buy-sell with cooldown
Previous Year Questions & Patterns
Repeat coding questions with the expected approach.
Software Engineer
- Find the minimum discount required to clear overstock (Binary search on answer).
- Find trending products within the last K minutes (Sliding window + frequency map).
- Traverse a product-category hierarchy using DFS/BFS.
- Design a shopping cart with add/remove and coupon validation logic.
- Design an inventory-reservation system handling temporary locks and concurrency.
- Find the second-largest distinct product price in one pass.
Analytics
- Find top 5 products by revenue in the last two months.
- Identify customers who purchased from more than four different cities.
- Find products that never received a return request (Left join / NOT EXISTS).
- Calculate average cart value per customer for the last three months.
- Identify customers who made at least two purchases every month for six months.
- Find products with revenue higher than their category-average revenue.
20+ SQL Query Bank
Write these out by hand — joins and subqueries are asked in both the test and the interview.
Revenue & Ranking
Top products
SELECT product_id, SUM(amount) AS total_revenue
FROM orders
WHERE order_date >= CURRENT_DATE - INTERVAL '2 months'
GROUP BY product_id
ORDER BY total_revenue DESC
LIMIT 5;Best brand by category
WITH brand_orders AS (
SELECT p.category, p.brand, COUNT(*) AS order_count
FROM orders o
JOIN products p ON o.product_id = p.product_id
GROUP BY p.category, p.brand
),
ranked AS (
SELECT category, brand, order_count,
RANK() OVER (PARTITION BY category ORDER BY order_count DESC) AS rnk
FROM brand_orders
)
SELECT category, brand, order_count FROM ranked WHERE rnk = 1;Behavioural analysis
High return sellers
SELECT seller_id
FROM orders
GROUP BY seller_id
HAVING 1.0 * SUM(CASE WHEN return_status = 'Returned' THEN 1 ELSE 0 END) / COUNT(*) > 0.06;Consecutive day buyers
WITH customer_days AS (
SELECT DISTINCT customer_id, order_date FROM orders
),
with_lag AS (
SELECT customer_id, order_date,
LAG(order_date, 1) OVER (PARTITION BY customer_id ORDER BY order_date) AS d1,
LAG(order_date, 2) OVER (PARTITION BY customer_id ORDER BY order_date) AS d2
FROM customer_days
)
SELECT DISTINCT customer_id FROM with_lag
WHERE order_date - d1 = INTERVAL '1 day' AND d1 - d2 = INTERVAL '1 day';DBMS & Core Interview Q&A
Short answers you should be able to say out loud.
What is the use of NTILE(4) in SQL?
It divides the result set into four roughly equal groups or quartiles for distribution analysis.
How to handle returns in SQL revenue calculation?
Use conditional aggregation (CASE WHEN) to subtract returned amounts or filter out 'Returned' status.
Why use CTEs for cohort analysis?
They improve readability when defining the initial user-cohort and joining it with subsequent transaction periods.
Difference between RANK and DENSE_RANK?
RANK leaves gaps in sequence after ties; DENSE_RANK provides continuous numbers without gaps.
OOPs Concepts + 10 Code Snippets
Theory plus the exact snippets asked in the technical round.
Composition over Inheritance
Nykaa design rounds prefer building complex products (variants, shades) by composing objects rather than deep hierarchies.
SOLID Principles
Critical for designing extensible coupon and discount engines that support multiple rules.
State Pattern
Used to model order lifecycles: Placed, Reserved, PaymentPending, Shipped, Delivered, Returned.
Strategy Pattern
Ideal for implementing multiple pricing or recommendation strategies that can be swapped at runtime.
Binary search for minimum discount
int minDiscount(vector<int>& stock, int targetClearance) {
int low = 0, high = 100, ans = 100;
while (low <= high) {
int mid = low + (high - low) / 2;
if (isFeasible(stock, mid, targetClearance)) {
ans = mid;
high = mid - 1;
} else {
low = mid + 1;
}
}
return ans;
}Python Category Summary (pandas)
# Aggregating revenue, orders, and AOV by category
category_summary = (
orders.groupby("category", as_index=False)
.agg(
revenue=("amount", "sum"),
orders=("order_id", "nunique"),
avg_order_value=("amount", "mean")
)
.sort_values("revenue", ascending=False)
)Operating System Basics
Scheduling, deadlock and memory management essentials.
Concurrency & Locks
- • Inventory race conditions
- • Optimistic vs Pessimistic locking
- • Temporary reservation expiry
- • Atomic updates for stock counts
Distributed Systems basics
- • Idempotent payments
- • CDN for product images
- • Caching bestselling categories
- • Notification retries and backoff
4-Week Preparation Roadmap
From zero to test-ready in 28 days.
SE: Arrays & Strings | Analytics: SQL basics
- 6 DSA problems / 10 SQL queries
Goal: Master basic e-commerce table structures and string manipulation.
SE: Sliding window & Binary search | Analytics: Joins & Dates
- 6 problems / 10 date-heavy queries
Goal: Handle trending products and time-based revenue analysis.
SE: Lists, Stacks, Queues | Analytics: Window functions
- 5 problems / 8 window function tasks
Goal: Learn ranking, quartiles, and running totals.
SE: Trees & Graphs | Analytics: Python/pandas
- 5 hierarchy problems / 2 pandas notebooks
Goal: Manage category trees and large-scale data cleaning.
SE: Dynamic Programming | Analytics: Statistics
- 5 DP problems / stats revision
Goal: Optimize complex decisions and learn A/B testing basics.
SE: OOP & LLD | Analytics: Product metrics
- 1 LLD design / metric definitions
Goal: Design cart systems and understand GMV/CAC/LTV.
SE: System Design | Analytics: Business cases
- 1 system HLD / 2 business cases
Goal: Explain recommendation engines and return-rate anomalies.
Timed OAs and Mock Interviews
- 1 timed test / 1 mock round
Goal: Simulate the pressure of a 90-minute coding assessment.
Exam Day Strategy
Score targets and time allocation.
Section targets
- Online Assessment: For SE: Code the simplest DSA problem first to secure marks, then tackle the LLD/Hard problem. For Analytics: Validate window function edge cases.
- Technical Rounds: Start with brute force, then optimize. For design, always clarify the scale and variant requirements (size, color, shade).
- Business Cases: Structure answers using: Problem → Metrics → Funnel Analysis → Hypotheses → Recommendation.
Coding time split (50 min)
- Software Engineer: 90 min DSA + 45 min timed coding + 45 min LLD + 30 min CS Fundamentals
- Analytics: 60 min SQL + 30 min Python/pandas + 30 min Statistics + 45 min Case Analysis
Interview Process
What follows the online test.
Software Engineer
- • Online assessment (2 coding problems, 90 min)
- • DSA interview (coding and complexity)
- • Technical/Design round (LLD, APIs, product scenarios)
- • Hiring manager (Practical thinking and ownership)
- • HR/Culture fit
Analytics
- • Online assessment (SQL, aptitude, data interpretation)
- • Technical/Data round (SQL, Python, statistics)
- • Business case (Revenue, returns, inventory, marketing)
- • Managerial/HR round
Mock Test Plan
Four simulations to run before the real test.
SE Mock: E-commerce DSA
- • 2 problems in 90 min
- • Include 1 hierarchy traversal
- • Include 1 sliding window problem
Analytics Mock: SQL Mastery
- • 10 queries in 60 min
- • Focus on RANK, LAG, and conditional sums
- • Return rate analysis task
Final Checklist
Tick all of these before test day.
Recent Interview Experiences
First-hand write-ups from students who actually sat the Nykaa process.
Be the first to share a Nykaa interview experience.
Frequently Asked Questions
Is Nykaa's OA hard?
It typically features medium-level DSA problems, but time management for two 45-minute problems is key.
How important is SQL for Software Engineers?
Mostly basic to medium; however, Analytics candidates must master advanced window functions.
What is a 'shade matching' problem?
A beauty-specific recommendation problem involving product Trial/Sample conversion and skin-type filtering.
Want the full structured prep for Nykaa?
The Premium Vault covers Aptitude, Core CS, DSA, SQL and HR rounds with module-wise courses and drill sheets.
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