Indian Unicorn

SDE-1, Product Analyst, Data Science

Online assessment (DSA/SQL/Stats) + machine coding + technical/design interviews

PhonePe SDE-1, Product Analyst & Data Science — Hiring Structure, Syllabus & 8-Week Prep Plan 2026

PhonePe is a premier Indian fintech unicorn with a high bar for engineering and analytics. The SDE-1 track emphasizes machine coding and reliable, scalable payment systems. The Product Analyst track focuses on SQL, experimentation, and business metrics, while Data Science centers on ML production, fraud detection, and statistics. This guide provides a role-specific breakdown of the syllabus, interview process, and practice questions.

SDE-1: focus on DSA, machine coding, and payment-system reliability. Product Analyst: focus on SQL, funnels, and experimentation. Data Science: focus on statistics, ML production, and fraud risk.

Last updated: 12 August 2026

Exam Pattern

SDE-1: focus on DSA, machine coding, and payment-system reliability. Product Analyst: focus on SQL, funnels, and experimentation. Data Science: focus on statistics, ML production, and fraud risk.

Section	Questions	Time	Type	Difficulty
SDE-1 · Online assessment	2–3 problems	60–90 min	DSA + SQL	Medium–Hard
SDE-1 · Machine coding	1 live task	60–90 min	Modular, testable code implementation	Hard
Product Analyst · Assessment	Mixed	Timed	SQL, aptitude, data interpretation	Medium
Data Science · Assessment	Mixed	Timed	Coding, SQL, statistics	Medium–Hard

Section-Wise Syllabus

Everything that appears in the assessment, grouped by section.

1. SDE-1 — DSA & Machine Coding

Core DSA

• Prefix sums
• Difference arrays
• Two pointers
• Sliding window
• Hashing
• Sorting-based optimization
• Coordinate compression
• Subarrays & subsequences
• Trie
• Segment Trees
• Multi-source BFS
• Strongly connected components

Machine Coding Tasks

• Rate limiter
• Inventory service
• Wallet ledger
• Notification engine
• Payment retry system
• Order or transaction service
• LRU cache
• Job scheduler

System Design (LLD/HLD)

• UPI payment flow
• Transaction state machine
• Idempotency
• Double-entry accounting
• Atomic updates
• Eventual consistency
• Reconciliation
• Audit trails
• Encryption & Auth

2. Product Analyst — SQL & Metrics

SQL Proficiency

• Joins & Aggregations
• Window functions (Rank, Lead, Lag)
• CTEs
• Date-time manipulation
• Conditional aggregation
• Cohort analysis
• Funnel analysis
• Retention queries
• Query optimization

Product Metrics

• MAU/DAU
• Activation rate
• Transaction success rate
• Average transaction value
• Take rate
• Churn & Retention
• CAC & LTV
• Fraud & Refund rates
• Support contact rate

Experimentation

• A/B testing design
• Sample size determination
• P-values & Significance
• Guardrail metrics
• Selection bias
• Counter-metrics

3. Data Science — Stats & ML

Statistics & Probability

• Bayes’ theorem
• Random variables
• Hypothesis testing
• Confidence intervals
• Central limit theorem
• Correlation vs Causation
• Bootstrap
• Bias-Variance tradeoff

Machine Learning

• Linear & Logistic regression
• Decision trees & Random forests
• Gradient boosting (XGBoost)
• Clustering & PCA
• Class imbalance handling
• Regularization
• Cross-validation
• Model calibration

ML Systems & Production

• Feature stores
• Feature engineering (Velocity, Graph)
• Online inference
• Model monitoring & drift
• Delayed fraud labels
• Model explainability
• Manual review queues

Most Asked Topics

Highest-frequency areas across recent test papers.

Idempotent payment design

Ensuring a request applied twice does not double-charge; core to SDE rounds.

Transaction success rate analysis

Segmenting drops by bank, method, device, or geography to find root causes.

Fraud detection metrics

Why accuracy fails (imbalance) and focusing on Precision, Recall, and PR-AUC.

Wallet ledger (Double-entry)

Maintaining append-only events rather than just a balance field for auditability.

Shortest path in networks

BFS/Dijkstra applied to settlement or payment graphs.

A/B testing pitfalls

Understanding p-hacking, selection bias, and choosing the right success metrics.

Free Resources

Curated practice links per section.

Coding & Design

Analytics & SQL

Data Science & ML

50+ DSA Practice Problems

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

Arrays & Strings

Longest substring without repetition
Minimum window substring
Maximum subarray sum
Product of array except self
Smallest missing positive integer
Group anagrams
Subarray sum K
Merge intervals
Longest consecutive sequence

Lists & Stacks

Reverse in groups of k
Find cycle-entry node
Clone random-pointer list
Largest rectangle in histogram
Sliding-window maximum
Min stack
Next greater element

Trees & Graphs

Serialize & deserialize tree
Lowest common ancestor
Validate BST
Root-to-leaf sums
Number of islands
Shortest path (Dijkstra)
Strongly connected components
Topological sort

DP & Heaps

House robber
Coin change
0/1 knapsack
Edit distance
Stock buy-sell
Bitmask DP basics
Top K items (Heap)
LRU Cache implementation

Previous Year Questions & Patterns

Repeat coding questions with the expected approach.

SDE-1 Design & Logic

Design an idempotent payment API.
Implement a rate limiter (Token Bucket or Leaky Bucket).
Design a wallet ledger using double-entry principles.
Design a notification system with retries and deduplication.
Find the shortest path in a settlement graph.
Implement an O(1) LRU Cache.

Product Analyst Cases

UPI success rate dropped by 5%. How would you investigate?
New-user activation declined after a product change. What do you check?
How would you measure the effectiveness of a cashback program?
How would you detect users abusing referral bonuses?
What metrics would you track for a new 'Wallet-to-Bank' feature?
How do you handle missing labels in transaction data?

Data Science & Stats

How would you handle highly imbalanced data (0.1% fraud)?
Explain Bayes' Theorem in the context of fraud detection.
How do you choose a threshold for a fraud model given false-positive costs?
What is model drift and how do you monitor it?
How do you prevent data leakage in time-series transaction models?
Explain the difference between L1 and L2 regularization.

20+ SQL Query Bank

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

Growth & Retention

Monthly cohort retention

WITH first_month AS (
  SELECT user_id, DATE_TRUNC('month', MIN(transaction_time)) AS cohort_month
  FROM transactions WHERE status = 'SUCCESS'
  GROUP BY user_id
),
activity AS (
  SELECT DISTINCT user_id, DATE_TRUNC('month', transaction_time) AS activity_month
  FROM transactions WHERE status = 'SUCCESS'
)
SELECT f.cohort_month, a.activity_month, COUNT(DISTINCT a.user_id) AS active_users
FROM first_month f JOIN activity a ON f.user_id = a.user_id
GROUP BY 1, 2;

Repeat users check

SELECT user_id
FROM transactions
WHERE status = 'SUCCESS'
GROUP BY user_id
HAVING COUNT(DISTINCT transaction_id) > 1;

Performance Metrics

Daily success rate

SELECT DATE(transaction_time) AS txn_date,
  100.0 * SUM(CASE WHEN status = 'SUCCESS' THEN 1 ELSE 0 END) / COUNT(*) AS success_rate
FROM transactions
GROUP BY 1 ORDER BY 1;

First successful transaction

SELECT user_id, MIN(transaction_time) AS first_success_time
FROM transactions
WHERE status = 'SUCCESS'
GROUP BY user_id;

DBMS & Core Interview Q&A

Short answers you should be able to say out loud.

What is the importance of 'idempotency' in payments?

It ensures that if an API request is retried (e.g., due to network timeout), the transaction is only processed once, preventing duplicate charges.

Why use a ledger instead of a simple balance column?

A ledger provides a full audit trail of every credit and debit, allowing for reconciliation and verification of the current balance at any point in time.

How do you handle 'Race Conditions' in a wallet system?

Using database transactions with appropriate isolation levels (like Serializable) or row-level locks (SELECT FOR UPDATE) to ensure balance updates are atomic.

What is a 'Dirty Read'?

When a transaction reads data that has been modified by another transaction but not yet committed; dangerous in financial systems.

OOPs Concepts + 10 Code Snippets

Theory plus the exact snippets asked in the technical round.

Interface-first design

Crucial for machine coding; defining clean interfaces for Services and Repositories before implementation.

State Machine Pattern

Used to manage complex transaction lifecycles (INITIATED -> PENDING -> SUCCESS/FAILED).

Strategy Pattern

Useful for handling different payment methods (UPI, Card, Wallet) with a unified interface.

SOLID Principles

PhonePe evaluates machine coding heavily on Single Responsibility and Open-Closed principles.

Transaction State Machine (Conceptual Java)

java

public enum TransactionStatus {
    INITIATED, PENDING, SUCCESS, FAILED, REFUNDED
}

public class Transaction {
    private String id;
    private TransactionStatus status;
    private String idempotencyKey;

    public void updateStatus(TransactionStatus newStatus) {
        // Validate state transition logic
        if (isValidTransition(this.status, newStatus)) {
            this.status = newStatus;
        }
    }
}

Rate Limiter Interface

java

public interface RateLimiter {
    /**
     * @return true if the request is allowed, false otherwise
     */
    boolean allowRequest(String clientId);
}

Funnel Conversion SQL

sql

SELECT 
    stage, 
    COUNT(DISTINCT user_id) AS users,
    LAG(COUNT(DISTINCT user_id)) OVER (ORDER BY stage_order) AS prev_stage_users
FROM funnel_events
GROUP BY stage, stage_order;

Operating System Basics

Scheduling, deadlock and memory management essentials.

Reliability & Concurrency

• Thread-safe balance updates
• Distributed locking for multi-node systems
• Atomic operations vs eventual consistency
• Optimistic vs Pessimistic locking in DBs
• Handling network timeouts and retries

ML Production (for Data Science)

• Serving latency (online inference)
• Batch vs Real-time feature generation
• Feature store consistency
• Model versioning and rollbacks
• Logging model inputs for offline debugging

4-Week Preparation Roadmap

From zero to test-ready in 28 days.

Week 1

Track Foundations

SDE: Arrays & Strings | PA: SQL Basics | DS: Stats

Goal: Establish core language/tooling proficiency.

Week 2

Intermediate Algorithms & Data

SDE: Linked Lists & Stacks | PA: Aggregations | DS: Regression

Goal: Master common coding patterns and basic modeling.

Week 3

Advanced Logic & Window Functions

SDE: Trees & Graphs | PA: Window Functions | DS: Feature Eng

Goal: Handle complex data relationships.

Week 4

Machine Coding & Imbalance

SDE: LLD Basics | PA: Python/Pandas | DS: Imbalance handling

Goal: Implement small systems and handle real-world data issues.

Week 5

System Design & Experiments

SDE: HLD & DP | PA: Metrics | DS: A/B Testing

Goal: Design for scale and scientific measurement.

Week 6

Fintech Specialization

SDE: Payment flow design | PA: Case studies | DS: Fraud cases

Goal: Understand domain-specific challenges.

Week 7

Mocks & Refinement

Live machine coding practice | SQL speed tests | ML system design

Goal: Build interview confidence.

Week 8

Final Sprints

Timed OAs | Technical & Behavioral mocks | Revision

Goal: Ensure readiness for the actual assessment.

Exam Day Strategy

Score targets and time allocation.

Section targets

Machine Coding: Spend first 10 mins on design/interfaces; ensure code is runnable and testable.
SQL Assessment: Read schema carefully; handle NULLs and duplicate records explicitly.
DSA Round: Start with brute force, then optimize; discuss time/space complexity clearly.
Product Case: Ask clarifying questions about the goal before proposing metrics.

Coding time split (50 min)

15% Design & Clarification
60% Implementation (Happy Path + Validation)
15% Testing & Edge Cases
10% Complexity Analysis & Scaling Discussion

Interview Process

What follows the online test.

SDE-1 Track

• OA (DSA + SQL)
• Machine Coding (60-90 min)
• DSA Interview (Problem solving)
• LLD/System Design (Payment focus)
• Hiring Manager (Fit & Ownership)

Product Analyst Track

• Assessment (Aptitude + SQL)
• SQL/Data Interview
• Product Case Study
• Analytical/Business Round
• HR Round

Data Science Track

• Resume Screen
• Technical Assessment (SQL + Coding)
• ML Technical Round
• Business Case (Fraud/Risk)
• ML System Round
• Managerial Round

Mock Test Plan

Four simulations to run before the real test.

SDE Machine Coding Mock

• Build a simple Rate Limiter in 90 mins.
• Requirements: Support different strategies, handle multiple clients, thread-safe implementation.

Analyst Case Mock

• Analyze a 5% drop in transaction success rate.
• Identify 3 primary dimensions for segmentation.
• Propose a fix and a measurement plan.

Final Checklist

Tick all of these before test day.

I understand the double-entry accounting principle.

I can design an idempotent API.

I can write SQL window functions for cohort analysis.

I understand how to handle class imbalance in ML.

I can explain the difference between precision and recall in fraud detection.

I have implemented at least two machine coding problems from scratch.

Recent Interview Experiences

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

Be the first to share a PhonePe interview experience.

Frequently Asked Questions

Does PhonePe focus more on HLD or LLD for SDE-1?

For SDE-1, the focus is heavily on LLD and Machine Coding. You should be able to produce working, modular code for a small system.

Is SQL mandatory for SDE roles?

Yes, PhonePe often includes a SQL question in the online assessment even for SDE roles, as their engineering involves heavy transaction processing.

What is the most important metric for PhonePe?

Transaction Success Rate (TSR) is central to their business, along with MAU and Fraud Rate.

Want the full structured prep for PhonePe?

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.

Computer Networks Essentials

OSI vs TCP/IP, HTTP, DNS and the networking questions freshers actually get.

Quantitative Aptitude Essentials

Shortcut-driven revision for the aptitude section of the online test.

Recommended practice path

Most Asked DSA Questions Sheet

High-frequency problems for candidates targeting PhonePe.