CRED SDE-1, Growth & Data — Hiring Process, Syllabus & 8-Week Prep Plan 2026
CRED hires for SDE-1, Growth, and Data tracks through a selective process emphasizing practical engineering, product judgment, and business analytics. SDE-1 focuses on clean code (SOLID) and fintech systems. Growth roles focus on experiment design and the AARRR funnel. Data roles prioritize SQL, risk analysis, and customer behavior.
SDE-1: DSA, machine coding (LLD), fintech system design. Growth: SQL, metrics, product case. Data: SQL, stats, Python, analytics case.
Last updated:
Exam Pattern
SDE-1: DSA, machine coding (LLD), fintech system design. Growth: SQL, metrics, product case. Data: SQL, stats, Python, analytics case.
| Section | Questions | Time | Type | Difficulty |
|---|---|---|---|---|
| SDE-1 · OA / Take-home | 2–3 problems | 90 min / 48 hr | DSA / Engineering Quality | Medium–Hard |
| SDE-1 · DSA & Puzzles | 1–2 problems | 60–90 min | Algorithms & Logic | Medium–Hard |
| SDE-1 · Machine Coding | 1 system | 90–120 min | LLD & SOLID implementation | Hard |
| SDE-1 · System Design | 1 scenario | 45–90 min | Fintech reliability & scalability | Hard |
| Growth · Data Assessment | Mixed | 60–90 min | SQL, Quant & Data Interpretation | Medium |
| Growth · Case Interview | 1–2 cases | 45–60 min | Funnel & Experiment design | Medium–Hard |
| Data · Online Test | Mixed | Timed | SQL, Stats, Python & Aptitude | Medium |
| Data · Analytics Case | 1 case | 60 min | User behavior, Payment risk & Fraud | Medium–Hard |
Section-Wise Syllabus
Everything that appears in the assessment, grouped by section.
1. SDE-1 — Technical Core
DSA Advanced
- • Prefix sums
- • Difference arrays
- • Sliding window
- • Hashing
- • Coordinate compression
- • Cycle decomposition
- • Bipartite graphs
- • Topological sort
- • Dijkstra
- • 0–1 BFS
- • Digit DP basics
- • Segment trees basics
Machine Coding (LLD)
- • Contact management system
- • Wallet ledger
- • Coupon engine
- • Rate limiter
- • LRU cache
- • Expense-sharing system
- • In-memory data structures
- • SOLID principles
- • Unit testing
- • Custom exceptions
Fintech System Design
- • Idempotent payment API
- • Append-only ledger
- • Transaction state machines
- • Wallet & Transaction history
- • Reconciliation services
- • Fraud-rule engines
- • Webhook reliability
- • Retry with backoff
2. Growth — Metrics & Funnels
Growth Funnel (AARRR)
- • Acquisition (App install)
- • Activation (Eligibility/Membership)
- • Engagement (Bill payments)
- • Retention (Repeat payments)
- • Monetisation (Lending/Rewards)
- • Referral conversion
Metrics & Analytics
- • DAU / MAU
- • Churn rate
- • Customer Lifetime Value (LTV)
- • Cost per Acquisition (CAC)
- • Campaign ROI
- • Average Revenue Per User (ARPU)
- • Incremental revenue
- • Incentive abuse monitoring
Experimentation
- • A/B Testing
- • Control vs Treatment
- • p-values & Statistical significance
- • Type I & Type II errors
- • Guardrail metrics
- • Sample size calculation
- • Novelty & Network effects
3. Data — SQL, Python & Stats
SQL for Analytics
- • Window functions (RANK, LEAD, LAG)
- • CTEs & Subqueries
- • Cohort analysis
- • Funnel queries
- • Retention logic
- • Gaps and islands
- • Duplicate detection
- • Conditional aggregation
Python & Statistics
- • Pandas pivot_table & groupby
- • Missing value handling
- • Mean/Median/Mode
- • Variance & Std Dev
- • Confidence intervals
- • Hypothesis testing
- • Correlation vs Causation
- • Imbalanced classification (Fraud)
Risk & Fraud
- • Transaction velocity
- • Device/IP signals
- • Account age analysis
- • Merchant risk
- • Model drift
- • Explainability
- • False positive cost
Most Asked Topics
Highest-frequency areas across recent test papers.
Idempotency in Payments
Ensuring a payment request applied twice doesn't double-charge; critical for fintech design.
Machine Coding (SOLID)
Implementing systems like a Wallet or Contact Manager with clean, extensible OOP principles.
A/B Testing Frameworks
Defining primary vs guardrail metrics and identifying statistical significance in growth experiments.
Cohort Analysis (SQL)
Writing queries to track user retention and repeat bill payment behavior over months.
Sliding Window Maximum
A frequent DSA problem involving monotonic queues or priority queues.
Risk & Fraud Detection
Analyzing device signals and velocity patterns to detect reward or incentive abuse.
Free Resources
Curated practice links per section.
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 (Kadane)
- Product of array except self
- Smallest missing positive integer
- Group anagrams
- Subarray sum equals K
- Merge intervals
- Minimum swaps to sort
- Longest consecutive sequence
Linked Lists & Stacks
- Reverse in groups of k
- Detect cycle & entry node
- Merge sorted lists
- Clone list with random pointers
- Balanced parentheses
- Min stack
- Next greater element
- Largest rectangle in histogram
- Sliding window maximum
- Queue using stacks
Trees & Graphs
- Lowest common ancestor
- Validate BST
- Serialize & deserialize tree
- Root-to-leaf paths
- Number of islands
- Dijkstra's shortest path
- Cycle detection (Directed/Undirected)
- Bipartite graph check
- Topological sorting
- DSU implementations
DP & Heaps
- House robber
- Coin change
- Longest increasing subsequence
- Edit distance
- Partition DP
- Top K elements
- Merge K sorted lists
Previous Year Questions & Patterns
Repeat coding questions with the expected approach.
SDE-1 (Machine Coding & DSA)
- Design a Contact Management System with search, group, and duplicate detection features.
- Design an idempotent payment API handling retries and duplicate callbacks.
- Implement a rate limiter using Token Bucket or Sliding Window.
- Design a cashback/rewards engine with eligibility rules and ledger reversals.
- Implement an LRU cache using a Hash map and Doubly Linked List.
- Find the minimum swaps to sort an array using cycle decomposition.
Growth Case Studies
- First bill payment conversion fell by 15%. How would you investigate? (Onboarding, eligibility, UI, segment analysis)
- Referral sign-ups increased but retained users declined. Why? (Incentive abuse, low-quality acquisition)
- How would you measure the incremental impact of a new cashback campaign?
- Should CRED prioritize acquisition or retention for the next quarter? Justify with metrics.
Data & SQL Challenges
- Write a SQL query to find users who made payments in three consecutive months.
- How would you investigate a sudden increase in payment failures? (Error codes, bank-wise split, app version)
- How do you handle missing financial records without silently imputing values?
- Identify users with reward abuse patterns (Multiple devices, high velocity, shared bank accounts).
20+ SQL Query Bank
Write these out by hand — joins and subqueries are asked in both the test and the interview.
Customer Behavior
Repeat bill payments
SELECT user_id
FROM bill_payments
WHERE status = 'SUCCESS'
GROUP BY user_id
HAVING COUNT(DISTINCT payment_id) > 1;Monthly retention cohort
WITH first_payment AS (
SELECT user_id, DATE_TRUNC('month', MIN(payment_time)) AS cohort_month
FROM bill_payments WHERE status = 'SUCCESS' GROUP BY user_id
),
activity AS (
SELECT DISTINCT user_id, DATE_TRUNC('month', payment_time) AS activity_month
FROM bill_payments WHERE status = 'SUCCESS'
)
SELECT f.cohort_month, a.activity_month, COUNT(DISTINCT a.user_id) AS active_users
FROM first_payment f JOIN activity a ON f.user_id = a.user_id
GROUP BY 1, 2;Fraud Detection
Reward abuse detection
SELECT user_id, COUNT(*) AS reward_count,
COUNT(DISTINCT device_id) AS devices_used,
COUNT(DISTINCT bank_account_id) AS bank_accounts_used
FROM rewards
GROUP BY user_id
HAVING COUNT(*) > 10 OR COUNT(DISTINCT device_id) > 3;DBMS & Core Interview Q&A
Short answers you should be able to say out loud.
What is an Idempotency Key?
A unique value sent by the client to the server to ensure that the same operation is not performed more than once.
How do you handle 'Gaps and Islands' in SQL?
Using window functions like ROW_NUMBER() minus a sequence to group consecutive records.
Why use an append-only ledger for payments?
For auditability, immutability, and ensuring a clear transaction history without modifying existing records.
What is the cost of a False Positive in fraud detection?
A legitimate customer being blocked, leading to poor user experience and lost revenue.
OOPs Concepts + 10 Code Snippets
Theory plus the exact snippets asked in the technical round.
SOLID Principles
The foundation of CRED's machine coding round: Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion.
Transaction State Machine
Modeling payment statuses (PENDING, SUCCESS, FAILED, REFUNDED) to ensure safe state transitions.
In-memory Indexing
Using appropriate data structures (HashMaps, Tries, Trees) to optimize search in LLD rounds.
Composition over Inheritance
Preferring building complex objects by combining simpler ones to increase flexibility.
Python/Pandas: Monthly Aggregation
# Equivalent logic in Python/Pandas
# monthly = payments.assign(
# month=payments['payment_time'].dt.to_period('M')
# ).groupby('month').agg({
# 'user_id': 'nunique',
# 'amount': 'sum'
# })Idempotent API Logic (Skeleton)
public Response processPayment(Request req) {
String key = req.getIdempotencyKey();
if (ledger.exists(key)) return ledger.get(key);
Transaction t = ledger.create(req);
Result res = gateway.charge(t);
ledger.update(t, res);
return res;
}Operating System Basics
Scheduling, deadlock and memory management essentials.
Atomicity & Concurrency
- • Handling race conditions in wallet balances
- • Database locking strategies (Pessimistic vs Optimistic)
- • Distributed transactions across services
- • Ensuring consistency in high-concurrency fintech apps
Reliability & Performance
- • Rate limiting strategies
- • Caching for high-read transaction history
- • Background job processing for notifications
- • Audit logging for every API state change
4-Week Preparation Roadmap
From zero to test-ready in 28 days.
SDE: Arrays & Hashing | Growth: Metrics | Data: SQL Basics
- 10 SQL basics
- 5 Array problems
- Growth funnel mapping
Goal: Establish core fundamentals for your track.
SDE: Sliding Window | Growth: Acquisition | Data: Joins
- Sliding window problems
- CAC/LTV analysis
- Complex Joins
Goal: Master data retrieval and sequence logic.
SDE: LL & Stacks | Growth: Retention | Data: Window Func
- Reverse group K
- Retention cohort analysis
- Rank/Lead/Lag queries
Goal: Understand temporal data and linear structures.
SDE: Trees & Graphs | Growth: Experiments | Data: Python
- Dijkstra's & BFS
- A/B test design
- Pandas groupby/merge
Goal: Learn complex relationships and data manipulation.
SDE: DP | Growth: Rewards Case | Data: Stats
- LIS/LCS problems
- Cashback ROI case
- Hypothesis testing
Goal: Optimize algorithms and business logic.
SDE: Machine Coding | Growth: Funnels | Data: Fraud
- LLD: Contact Manager
- Funnel drop analysis
- Risk modeling
Goal: Apply skills to end-to-end scenarios.
SDE: Fintech Design | Growth: Mock Cases | Data: Mocks
- Payment gateway design
- Mock interviews
- Case study practice
Goal: Synthesize knowledge for high-level rounds.
Final Review & Mocks
- Timed OAs
- Resume pitching
- Data storytelling
Goal: Achieve interview readiness.
Exam Day Strategy
Score targets and time allocation.
Section targets
- Machine Coding: Prioritize working code and SOLID principles over extra features.
- DSA Round: Think out loud, discuss complexity, and handle edge cases (nulls, empty inputs).
- Growth Case: Structured thinking: Objective -> Funnel -> Hypotheses -> Experiment -> Metrics.
- Data/SQL: Verify join logic and null handling; explain the business impact of your data findings.
Coding time split (50 min)
- Requirement clarification: 10 min
- Design & Interfaces: 20 min
- Core Implementation: 60 min
- Validation & Testing: 20 min
- Discussion & Extensibility: 10 min
Interview Process
What follows the online test.
Selective Screening
- • Rigorous resume/profile screening
- • High focus on project depth and engineering quality
- • Recruiter screen for motivation and culture fit
Technical Evaluation
- • Take-home assignment or OA
- • Live DSA and machine coding rounds
- • Deep dive into core technology stack
Product & Culture
- • Product judgment and user empathy discussions
- • Bar-raiser or managerial round for ownership
- • Culture-fit interview (CRED values)
Mock Test Plan
Four simulations to run before the real test.
SDE-1 Mock
- • 90-min Machine Coding: Wallet System
- • 45-min DSA: Graphs & DP
- • 60-min System Design: Payment Gateway
Growth/Data Mock
- • 60-min SQL & Case: User Retention Drop
- • 45-min Stats & A/B Testing
- • 45-min Product Thinking: Rewards Engine
Final Checklist
Tick all of these before test day.
Recent Interview Experiences
First-hand write-ups from students who actually sat the CRED process.
Be the first to share a CRED interview experience.
Frequently Asked Questions
Does CRED focus on Competitive Programming?
Partially; they value DSA but prioritize engineering quality (clean code, SOLID) and system design more than pure speed.
What language is preferred for Machine Coding?
Java is common, but you can use Python or C++ as long as you follow clean OOP practices.
How important is the product case for non-tech roles?
Crucial. You must understand CRED's specific business (credit cards, rewards, payments) and offer data-backed solutions.
Want the full structured prep for CRED?
The Premium Vault covers Aptitude, Core CS, DSA, SQL and HR rounds with module-wise courses and drill sheets.
Related Prep
Next steps chosen from the CRED pattern and other Indian Unicorn recruiters.
Other Indian Unicorn companies
Flipkart Interview Prep Guide
Flipkart exam pattern, syllabus and previous year questions · ₹18–32 LPA · intern ₹80k–1.2L/mo.
Swiggy Interview Prep Guide
Swiggy exam pattern, syllabus and previous year questions · ₹16–30 LPA.
Zomato & Blinkit Interview Prep Guide
Zomato & Blinkit exam pattern, syllabus and previous year questions · ₹14–28 LPA.
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