Indian Unicorn

SWE, Data Analyst

Online assessment (HackerRank) + Technical (DSA, Core CS, System Design) + Managerial rounds

Delhivery SWE & Data Analyst — Hiring Structure, Syllabus & 8-Week Prep Plan 2026

Delhivery hiring focuses on two tracks: SWE (coding and systems) and Data Analyst (SQL, statistics, and logistics metrics). Given its domain, focus on routing, hub capacity, delivery SLAs, and operational data flow is crucial.

SWE: 90-min HackerRank (1–3 DSA) + 2–3 Technical rounds. Data Analyst: SQL/Excel assessment + Case Study + Technical interview.

Last updated: 12 August 2026

Exam Pattern

SWE: 90-min HackerRank (1–3 DSA) + 2–3 Technical rounds. Data Analyst: SQL/Excel assessment + Case Study + Technical interview.

Section	Questions	Time	Type	Difficulty
SWE · Online assessment	1–3 problems	60–90 min	DSA / Coding	Medium–Hard
SWE · DSA technical round	1–2 problems	45–60 min	Live coding	Medium–Hard
SWE · Core technical round	Varies	45–60 min	DBMS, OS, OOP, Project	Medium
SWE · System design (LLD/HLD)	1 case	45–60 min	Logistics systems & scalability	Hard
Data Analyst · SQL/Excel assessment	Mixed	Timed	Queries, formulas, interpretation	Medium
Data Analyst · Case round	1–2 cases	45–60 min	Logistics supply-chain solving	Medium–Hard
Data Analyst · Technical interview	Varies	45–60 min	SQL, Stats, Python, Projects	Medium–Hard

Section-Wise Syllabus

Everything that appears in the assessment, grouped by section.

1. SWE — DSA & Algorithms

Arrays & Strings

• Prefix sums
• Difference arrays
• Two pointers
• Sliding window
• Hashing
• Sorting-based optimisation
• Subarrays & subsequences
• Frequency counting
• Palindromes
• Anagrams
• String matching
• Minimum window
• Coordinate compression

Linked Lists & Stacks

• Reverse linked list
• Remove loop (Floyd cycle detection)
• Find cycle-entry node
• Merge sorted lists
• Clone random-pointer list
• Balanced parentheses
• Min stack
• Next greater element
• Largest rectangle in histogram
• Sliding-window maximum
• Queue using stacks

Trees & Graphs

• Tree traversals
• Height and diameter
• Lowest common ancestor
• Validate BST
• Serialize & deserialize
• BFS and DFS
• Connected components
• Cycle detection
• Bipartite graph
• Topological sorting
• Dijkstra
• Minimum spanning tree
• DSU
• Strongly connected components
• Multi-source BFS

Dynamic Programming

• House robber
• Coin change
• Knapsack
• Subset sum
• Grid DP
• LIS
• LCS
• Edit distance
• Stock buy-sell
• Partition DP
• Interval DP
• Tree DP

2. SWE — Core CS & Backend

OOP

• Encapsulation
• Abstraction
• Inheritance
• Polymorphism
• Overloading vs Overriding
• Virtual functions
• Interfaces
• Composition vs Inheritance
• SOLID principles

DBMS & SQL

• Keys
• Normalisation
• Joins
• Aggregation
• GROUP BY / HAVING
• Subqueries
• CTEs
• Window functions
• Indexing
• ACID properties
• Isolation levels
• Transactions
• Deadlocks
• Query optimisation

Operating Systems

• Process vs Thread
• Context switching
• Scheduling
• Synchronisation
• Mutex and Semaphore
• Deadlock
• Paging
• Virtual memory
• File systems

Networks & Scalability

• TCP vs UDP
• HTTP / HTTPS
• DNS
• Load balancing
• Caching
• Message queues (Kafka/RabbitMQ)
• Pub/sub
• Retries & Timeouts
• Circuit breakers
• Idempotency
• REST APIs
• Monitoring

3. Data Analyst — SQL, Stats & Excel

Advanced SQL

• Joins
• Aggregation
• GROUP BY / HAVING
• Subqueries
• CTEs
• Window functions (ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD)
• Date functions
• Conditional aggregation
• Null handling
• Cohorts
• Running totals
• Duplicate detection

Excel & Analytics

• VLOOKUP / XLOOKUP
• INDEX & MATCH
• SUMIF / COUNTIF / AVERAGEIF
• Pivot tables
• Conditional formatting
• Data cleaning
• Date calculations
• Duplicate handling
• Charts
• Scenario analysis
• Solver basics
• Dashboard design

Python & pandas

• Lists, dictionaries, sets
• NumPy
• pandas (groupby, merge, pivot_table)
• Missing-value handling
• Duplicate removal
• Date-time conversion
• Aggregation
• Basic visualisation

Statistics

• Mean, median, mode
• Variance & standard deviation
• Percentiles
• Probability
• Sampling
• Confidence intervals
• Hypothesis testing
• A/B testing
• Correlation vs Causation
• Regression basics
• Outlier analysis
• Linear programming basics

Most Asked Topics

Highest-frequency areas across recent test papers.

Logistics System Design

Designing shipment-tracking systems, hub-capacity services, and delivery-partner assignment logic.

Graph Shortest Path

Route optimisation problems using Dijkstra or BFS/DFS in a network of hubs.

Logistics Metrics

SLA violations, on-time delivery rate, RTO (Return-to-Origin) rate, and hub throughput.

SQL Window Functions

Rankings, lag/lead for shipment state transitions, and running totals for inventory.

LRU Cache & Merge Sort

Repeatedly reported in older and recent SWE technical interviews.

Shipment State Machine

Handling 'Created', 'Picked Up', 'In Transit', and 'Delivered' states with duplicate/out-of-order event handling.

Geospatial Indexing

Nearest-driver lookup and route compatibility for delivery allocation.

Free Resources

Curated practice links per section.

SWE Preparation

Data Analyst & SQL

System Design

50+ DSA Practice Problems

Solve in order — arrays and strings first, then sorting and recursion.

Arrays & Strings

Find all pairs summing to K
Index where left sum = right sum
Spiral matrix traversal
Maximum subarray sum (Kadane)
Longest substring without repetition
Smallest missing positive integer
Product of array except self
Merge intervals
Longest consecutive sequence

Linked Lists & Stacks

Reverse linked list
Remove loop from linked list
LRU Cache implementation
Min stack
Next greater element
Largest rectangle in histogram
Sliding window maximum

Trees & Graphs

Lowest Common Ancestor
Serialize and Deserialize BST
Shortest path in hub network (Dijkstra)
Course Schedule (Topological Sort)
Number of Islands (BFS/DFS)
Strongly Connected Components

DP & Sorting

Merge Sort implementation
Sort 0s, 1s, 2s (Dutch National Flag)
Edit Distance
Coin Change
0/1 Knapsack
LIS & LCS

Previous Year Questions & Patterns

Repeat coding questions with the expected approach.

SWE Coding (Previous Year)

Find all pairs in an array whose sum equals X.
Find an index where the sum on the left equals the sum on the right.
Implement merge sort and discuss O(n log n) complexity.
Implement an LRU cache using a hash map and doubly linked list.
Print a matrix in spiral order using four boundaries.
Remove a loop from a linked list using Floyd cycle detection.
Sort an array of 0s, 1s, and 2s (Dutch National Flag algorithm).

Logistics System Design

Design a shipment-tracking system (handling out-of-order events).
Design a hub-capacity service (forecasting, bottleneck detection).
Design nearest-delivery-partner assignment (geospatial indexing, capacity).
How would you handle duplicate scan events in a shipment state machine?
Design a system to handle Return-to-Origin (RTO) workflows.

Data Analyst Cases

Delivery delays increased by 20%. How would you investigate? (hub, route, weather, staffing).
RTO rate increased. What could cause it? (address quality, COD behavior, attempts).
How would you identify bottleneck hubs using inbound volume and processing time?
Calculate driver utilisation: Productive time vs available time.
Evaluate a new routing algorithm using late rate and cost per shipment.
A hub processes more shipments but its SLA worsens. Why? (overload, mix, bad prioritization).

20+ SQL Query Bank

Write these out by hand — joins and subqueries are asked in both the test and the interview.

Logistics Queries

Delivery success rate

SELECT DATE(delivery_time) AS delivery_date,
       100.0 * SUM(CASE WHEN status = 'DELIVERED' THEN 1 ELSE 0 END) / COUNT(*) AS delivery_success_rate
FROM shipments
GROUP BY DATE(delivery_time)
ORDER BY delivery_date;

Avg delivery time by hub

SELECT origin_hub,
       AVG(EXTRACT(EPOCH FROM (delivery_time - pickup_time)) / 3600) AS avg_delivery_hours
FROM shipments
WHERE status = 'DELIVERED'
GROUP BY origin_hub
ORDER BY avg_delivery_hours DESC;

Late-delivery rate by city

SELECT destination_city,
       100.0 * SUM(CASE WHEN actual_delivery > promised_delivery THEN 1 ELSE 0 END) / COUNT(*) AS late_rate
FROM shipments
GROUP BY destination_city
ORDER BY late_rate DESC;

Customer & Volume

Top hubs by volume

SELECT hub_id, COUNT(*) AS shipment_count
FROM shipment_events
GROUP BY hub_id
ORDER BY shipment_count DESC
LIMIT 10;

Repeat business customers

SELECT customer_id
FROM shipments
GROUP BY customer_id
HAVING COUNT(DISTINCT shipment_id) > 1;

DBMS & Core Interview Q&A

Short answers you should be able to say out loud.

What are the common states in a Delhivery shipment state machine?

Created -> Pickup Scheduled -> Picked Up -> In Transit -> At Hub -> Out for Delivery -> Delivered (also Cancelled, Lost, Damaged, RTO).

How do you handle out-of-order scan events?

Use sequence numbers or timestamps; ensure the state machine only permits valid forward transitions or updates historical logs without regressing the current state.

What is an index in DBMS and why use it for hub IDs?

A data structure (B-Tree/Hash) that improves data retrieval speed. For hub IDs, it speeds up queries filtering by origin or destination hub.

Explain ACID properties in the context of a payment/delivery transaction.

Atomicity (full payment or none), Consistency (valid state), Isolation (separate concurrent orders), Durability (logs saved even if server crashes).

Difference between ROW_NUMBER() and RANK() in SQL?

ROW_NUMBER() gives unique serial numbers; RANK() gives the same number to ties and skips subsequent numbers.

OOPs Concepts + 10 Code Snippets

Theory plus the exact snippets asked in the technical round.

Shipment State Pattern

Encapsulate state-specific behavior (e.g., 'PickedUpState', 'InTransitState') to manage transitions cleanly.

Composition over Inheritance

A Shipment might 'have' a Route and a DeliveryPartner rather than 'being' a type of Route.

SOLID: Single Responsibility

The NotificationService should only handle alerts, not shipment state changes or routing logic.

Idempotency in APIs

Designing APIs so that the same request (e.g., 'Deliver Order') can be called multiple times without side effects.

LRU Cache Implementation (Doubly Linked List + Map)

java

class LRUCache {
    class Node {
        int key, value;
        Node prev, next;
    }
    private Map<Integer, Node> map = new HashMap<>();
    private Node head, tail;
    private int capacity;

    public int get(int key) {
        Node node = map.get(key);
        if (node == null) return -1;
        moveToHead(node);
        return node.value;
    }

    public void put(int key, int value) {
        Node node = map.get(key);
        if (node == null) {
            node = new Node();
            node.key = key; node.value = value;
            map.put(key, node);
            addNode(node);
            if (map.size() > capacity) {
                Node last = popTail();
                map.remove(last.key);
            }
        } else {
            node.value = value;
            moveToHead(node);
        }
    }
}

Spiral Matrix Traversal

cpp

vector<int> spiralOrder(vector<vector<int>>& matrix) {
    int top = 0, bottom = matrix.size()-1;
    int left = 0, right = matrix[0].size()-1;
    vector<int> res;
    while(top <= bottom && left <= right) {
        for(int i=left; i<=right; i++) res.push_back(matrix[top][i]);
        top++;
        for(int i=top; i<=bottom; i++) res.push_back(matrix[i][right]);
        right--;
        if(top <= bottom) {
            for(int i=right; i>=left; i--) res.push_back(matrix[bottom][i]);
            bottom--;
        }
        if(left <= right) {
            for(int i=bottom; i>=top; i--) res.push_back(matrix[i][left]);
            left++;
        }
    }
    return res;
}

Logistics Hub Summary (pandas)

java

-- Hub Volume vs SLA Breach
SELECT hub_id, 
       COUNT(*) as total_shipments,
       SUM(CASE WHEN actual_delivery > promised_delivery THEN 1 ELSE 0 END) as sla_breaches
FROM shipments
GROUP BY hub_id
HAVING COUNT(*) > 100
ORDER BY sla_breaches DESC;

Operating System Basics

Scheduling, deadlock and memory management essentials.

Process vs Thread

• Processes have separate memory space; threads share it.
• Delhivery backend might use multiple threads for handling concurrent scan events.
• Context switching cost is lower for threads.

Deadlock Conditions

• Mutual Exclusion, Hold and Wait, No Preemption, Circular Wait.
• Crucial for inventory locking systems in warehouses.

Paging & Virtual Memory

• Managing large logistics datasets in memory.
• Handling page faults and swapping efficiency.

4-Week Preparation Roadmap

From zero to test-ready in 28 days.

Week 1-2

DSA Foundations & SQL Basics

Arrays, Strings, Hashing
Basic SQL Joins & Aggregates

Goal: Master array manipulation and basic data extraction.

Week 3-4

Advanced DSA & Python/pandas

Trees, Graphs, Dijkstra
pandas DataFrames & grouping

Goal: Learn routing algorithms and data cleaning.

Week 5-6

System Design & Statistics

Shipment tracking LLD
Hypothesis testing & A/B tests

Goal: Understand scalable logistics and experimentation.

Week 7-8

Full Mocks & Case Studies

Logistics business cases
HackerRank timed mocks

Goal: Simulate interview pressure and role-specific scenarios.

Exam Day Strategy

Score targets and time allocation.

Section targets

Online Coding: Solve 2/3 problems; focus on edge cases like empty arrays or huge inputs.
Data Case Round: Structure your thoughts: Define metrics, segments, hypotheses, and recommendations.
Technical Interview: Think out loud; explain time/space complexity before coding.

Coding time split (50 min)

90 min DSA
45 min System Design
45 min SQL/Stats
20 min Project Discussion

Interview Process

What follows the online test.

Track A: SWE

• HackerRank Assessment
• 2-3 Technical Rounds (DSA/Core CS)
• System Design (LLD/HLD)
• Hiring Manager

Track B: Data Analyst

• SQL/Excel Assessment
• Business Case Study
• Technical Interview (Python/Stats)
• HR Manager

Mock Test Plan

Four simulations to run before the real test.

SWE HackerRank Mock

• 3 Problems: 1 Easy (Array), 1 Medium (Graph), 1 Hard (DP)
• 90-minute limit

Data Analyst Case Mock

• Case: 'SLA breach in Hub X increased by 15%'
• Analyze metrics and propose solutions

Final Checklist

Tick all of these before test day.

Master LRU Cache and Merge Sort implementations

Understand shipment state transitions and event handling

Practice SQL window functions and cohort analysis

Revise Dijkstra's algorithm for shortest-path routing

Prepare project deep-dives focusing on scalability

Recent Interview Experiences

First-hand write-ups from students who actually sat the Delhivery process.

Be the first to share a Delhivery interview experience.

Frequently Asked Questions

What is the most important topic for Delhivery SWE?

DSA (Graphs/Arrays) and System Design related to tracking and routing.

Is SQL important for SWE roles?

Yes, basic to medium SQL (Joins, ACID) is frequently asked in core technical rounds.

What kind of math is asked in Data Analyst rounds?

Probability, statistics, mental maths, and sometimes linear programming basics.

Want the full structured prep for Delhivery?

The Premium Vault covers Aptitude, Core CS, DSA, SQL and HR rounds with module-wise courses and drill sheets.

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Core CS subjects for this pattern

DBMS & SQL Essentials

Normalisation, keys, transactions and the SQL queries asked in written rounds.

OOPs Essentials for Interviews

The four pillars with the exact code snippets interviewers ask you to explain.

Operating System Essentials

Processes, scheduling, deadlock and memory management in interview-ready form.

Recommended practice path

Most Asked DSA Questions Sheet

High-frequency problems for candidates targeting Delhivery.