Introduction

This post provides a comprehensive list of common system design interview questions for Senior Backend Software Engineer positions. These questions focus on backend systems, trading infrastructure, real-time data processing, and fintech-specific challenges at scale.

Note: For detailed preparation strategies, see the System Design Interview Guide.

Backend System Design Interview Overview

Interview Format

  • Duration: 45-60 minutes
  • Format: System design interview
  • Focus: Backend architecture, distributed systems, trading infrastructure
  • Process: Discussion and whiteboarding/virtual board
  • Evaluation: Problem-solving, technical depth, fintech expertise

Key Evaluation Areas

  1. Backend Architecture: Design scalable, reliable backend systems
  2. Trading Infrastructure: Understanding of trading systems and market data
  3. Real-Time Systems: Low-latency data processing and updates
  4. Data Systems: Databases, caching, message queues for financial data
  5. Reliability and Compliance: Fault tolerance, audit trails, regulatory requirements

Common Backend System Design Questions

Trading and Order Management Systems (Highest Priority)

These questions focus on the core trading infrastructure that fintech companies operate.

1. Design a Real-Time Order Execution System

Key Features:

  • Place and execute buy/sell orders
  • Order matching engine
  • Order routing to market makers
  • Real-time order status updates
  • Order cancellation and modification

Challenges:

  • Ultra-low latency (microseconds)
  • High throughput (millions of orders per second)
  • Order matching algorithms
  • Market data integration
  • Regulatory compliance (NMS, best execution)
  • Order audit trail

Key Components:

  • Order gateway
  • Order matching engine
  • Market data feed handler
  • Order router
  • Order persistence layer
  • Audit logging system

2. Design a Limit Order Book System

Key Features:

  • Maintain buy/sell order book
  • Price-time priority matching
  • Order level updates
  • Real-time best bid/offer (BBO)
  • Market depth visualization

Challenges:

  • High-frequency updates (millions per second)
  • Low-latency order matching
  • Memory-efficient data structures
  • Order book reconstruction
  • Snapshot and incremental updates

Key Components:

  • Order book data structure (red-black tree, heap)
  • Order matching algorithm
  • Market data processor
  • Order book snapshot service
  • Real-time update stream

3. Design a Portfolio Management System

Key Features:

  • Real-time portfolio valuation
  • Position tracking
  • P&L calculation
  • Historical portfolio performance
  • Multi-account support

Challenges:

  • Real-time price updates
  • Accurate position tracking
  • Consistent P&L calculation
  • High read throughput
  • Data consistency across services

Key Components:

  • Position service
  • Pricing service
  • Portfolio aggregation service
  • Historical data service
  • Cache layer

4. Design a Market Data Distribution System

Key Features:

  • Real-time market data ingestion
  • Data distribution to multiple consumers
  • Data normalization and transformation
  • Historical data storage
  • Market data replay

Challenges:

  • High-volume data streams (millions of messages per second)
  • Low-latency distribution
  • Data normalization
  • Multiple data sources (exchanges, market makers)
  • Data quality and validation

Key Components:

  • Market data feed handlers
  • Message queue (Kafka)
  • Data normalization service
  • Distribution service
  • Historical data store

5. Design a Risk Management System

Key Features:

  • Real-time risk checks
  • Position limits
  • Exposure limits
  • Margin calculations
  • Risk alerts and notifications

Challenges:

  • Real-time risk calculations
  • Low-latency risk checks
  • Complex risk rules
  • Regulatory compliance
  • Risk aggregation across accounts

Key Components:

  • Risk engine
  • Risk rules engine
  • Position aggregator
  • Margin calculator
  • Alert service

Payment and Banking Systems (High Priority)

These questions focus on financial transactions and banking infrastructure.

6. Design a Payment Processing System

Key Features:

  • Process deposits and withdrawals
  • ACH transfers
  • Wire transfers
  • Payment gateway integration
  • Transaction status tracking

Challenges:

  • Transaction reliability
  • Idempotency
  • Fraud detection
  • Regulatory compliance (AML, KYC)
  • Integration with banking partners
  • Transaction reconciliation

Key Components:

  • Payment gateway
  • Transaction processor
  • Fraud detection service
  • Banking partner integration
  • Reconciliation service
  • Audit logging

7. Design a Banking Integration System

Key Features:

  • Bank account linking
  • Account verification
  • Balance checking
  • Transaction history
  • Plaid/ACH integration

Challenges:

  • Third-party API integration
  • Data synchronization
  • Error handling and retries
  • Rate limiting
  • Security and encryption

Key Components:

  • Banking API client
  • Account verification service
  • Data sync service
  • Error handling layer
  • Security layer

8. Design a Transaction Reconciliation System

Key Features:

  • Match transactions across systems
  • Identify discrepancies
  • Automated reconciliation
  • Manual reconciliation workflows
  • Reconciliation reports

Challenges:

  • High-volume transaction matching
  • Data consistency
  • Discrepancy detection
  • Automated resolution
  • Audit trail

Key Components:

  • Transaction matcher
  • Discrepancy detector
  • Reconciliation engine
  • Reporting service
  • Audit logging

Real-Time Data and Analytics (High Priority)

These questions focus on real-time data processing and analytics for trading.

9. Design a Real-Time Analytics System

Key Features:

  • Real-time trade analytics
  • Performance metrics
  • User behavior tracking
  • Trading pattern analysis
  • Real-time dashboards

Challenges:

  • Real-time event processing
  • High-volume data streams
  • Low-latency aggregations
  • Complex analytics queries
  • Data retention

Key Components:

  • Stream processing engine (Kafka Streams, Flink)
  • Aggregation service
  • Analytics database
  • Dashboard service
  • Data retention service

10. Design a Real-Time Price Update System

Key Features:

  • Real-time stock price updates
  • Price aggregation from multiple sources
  • Price history storage
  • Price change notifications
  • WebSocket distribution

Challenges:

  • Ultra-low latency (milliseconds)
  • High-frequency updates (thousands per second per symbol)
  • WebSocket connection management
  • Price aggregation logic
  • Memory-efficient price storage

Key Components:

  • Price ingestion service
  • Price aggregator
  • WebSocket server
  • Price cache
  • Historical price store

11. Design a Time-Series Database System

Key Features:

  • Store time-series data (prices, trades, metrics)
  • Efficient time-range queries
  • Data compression
  • High write throughput
  • Data retention policies

Challenges:

  • High write throughput
  • Efficient time-range queries
  • Data compression
  • Storage optimization
  • Query performance

Key Components:

  • Time-series database (InfluxDB, TimescaleDB)
  • Data ingestion pipeline
  • Compression service
  • Query engine
  • Retention policy manager

Backend Infrastructure Questions (Medium Priority)

These questions focus on general backend infrastructure and distributed systems.

12. Design a Distributed Job Scheduler System

Key Features:

  • Schedule and execute background jobs
  • Job dependencies
  • Job retry and failure handling
  • Job monitoring and logging
  • SLA enforcement

Challenges:

  • Distributed scheduling
  • Job dependencies
  • Fault tolerance
  • Resource allocation
  • Audit trail for compliance

Key Components:

  • Job scheduler
  • Execution engine
  • Dependency manager
  • Monitoring service
  • Audit logging

See Also: Detailed Job Scheduler Design

13. Design a Distributed Cache System

Key Features:

  • High-performance caching
  • Cache invalidation strategies
  • Distributed caching
  • Cache consistency
  • Cache warming

Challenges:

  • Cache coherence
  • Distributed caching
  • Cache invalidation
  • Network partitioning
  • Performance optimization

Key Components:

  • Cache layer (Redis cluster)
  • Cache invalidation service
  • Consistency manager
  • Cache warming service
  • Monitoring service

14. Design a Message Queue System

Key Features:

  • Asynchronous message processing
  • Message ordering guarantees
  • Message persistence
  • Dead letter queues
  • Message replay

Challenges:

  • High throughput
  • Message ordering
  • Durability guarantees
  • Consumer scaling
  • Message replay

Key Components:

  • Message broker (Kafka, RabbitMQ)
  • Producer service
  • Consumer service
  • Dead letter queue handler
  • Message replay service

15. Design a Notification System

Key Features:

  • Push notifications
  • Email notifications
  • SMS notifications
  • Notification preferences
  • Delivery tracking

Challenges:

  • Multi-channel delivery
  • High throughput
  • Delivery reliability
  • User preferences
  • Rate limiting

Key Components:

  • Notification service
  • Push notification service
  • Email service
  • SMS service
  • Preference manager

Data and Storage Systems (Medium Priority)

These questions focus on data storage and database design.

16. Design a Financial Data Warehouse

Key Features:

  • Store historical financial data
  • OLAP queries
  • Data aggregation
  • ETL pipelines
  • Data partitioning

Challenges:

  • Large data volumes
  • Complex queries
  • Data partitioning
  • ETL performance
  • Query optimization

Key Components:

  • Data warehouse (Snowflake, Redshift)
  • ETL pipeline
  • Query engine
  • Data partitioning service
  • Analytics service

17. Design a User Data Service

Key Features:

  • User profile management
  • User preferences
  • Account information
  • KYC/AML data
  • Data privacy compliance

Challenges:

  • Data consistency
  • Privacy compliance (GDPR, CCPA)
  • High read throughput
  • Data encryption
  • Audit trail

Key Components:

  • User service
  • Profile database
  • Preference service
  • Compliance service
  • Audit logging

18. Design a Transaction Log System

Key Features:

  • Immutable transaction logs
  • Transaction replay
  • Audit trail
  • Compliance reporting
  • Data retention

Challenges:

  • Immutable storage
  • High write throughput
  • Efficient querying
  • Long-term retention
  • Compliance requirements

Key Components:

  • Transaction log store
  • Write service
  • Replay service
  • Query service
  • Retention manager

Security and Compliance Systems (Medium Priority)

These questions focus on security and regulatory compliance.

19. Design an Authentication and Authorization System

Key Features:

  • User authentication
  • Multi-factor authentication (MFA)
  • OAuth integration
  • Role-based access control (RBAC)
  • Session management

Challenges:

  • Security and encryption
  • High availability
  • Session management
  • MFA integration
  • OAuth flows

Key Components:

  • Authentication service
  • MFA service
  • OAuth provider
  • Authorization service
  • Session manager

20. Design an Audit Logging System

Key Features:

  • Comprehensive audit logs
  • Immutable logging
  • Compliance reporting
  • Log search and querying
  • Long-term retention

Challenges:

  • Immutable logs
  • High write volume
  • Compliance requirements
  • Log search performance
  • Long-term storage

Key Components:

  • Audit log service
  • Log storage
  • Search service
  • Compliance reporting
  • Retention manager

21. Design a Fraud Detection System

Key Features:

  • Real-time fraud detection
  • Pattern recognition
  • Risk scoring
  • Fraud alerts
  • Machine learning integration

Challenges:

  • Real-time detection
  • Low false positive rate
  • Machine learning models
  • Pattern recognition
  • Scalability

Key Components:

  • Fraud detection engine
  • ML model service
  • Risk scorer
  • Alert service
  • Pattern database

API and Integration Systems (Lower Priority)

22. Design a RESTful API Gateway

Key Features:

  • API routing
  • Rate limiting
  • Authentication
  • Request/response transformation
  • API versioning

Challenges:

  • High throughput
  • Low latency
  • Rate limiting
  • API versioning
  • Request routing

23. Design a WebSocket Service

Key Features:

  • Real-time data streaming
  • Connection management
  • Message broadcasting
  • Connection scaling
  • Heartbeat management

Challenges:

  • Connection scaling
  • Message broadcasting
  • Connection management
  • Heartbeat handling
  • Reconnection logic

24. Design a Third-Party Integration System

Key Features:

  • Third-party API integration
  • Rate limiting
  • Error handling and retries
  • Data transformation
  • Monitoring and alerting

Challenges:

  • API rate limits
  • Error handling
  • Data transformation
  • Monitoring
  • Retry strategies

Question Categories by Frequency

Tier 1: Most Common Questions (Must Practice) - 70%+ of Interviews

These questions are the core of backend system design interviews. Master these first as they appear in the majority of interviews.

1. Design a Real-Time Order Execution System

Why It’s Critical:

  • Core to trading platforms
  • Demonstrates understanding of ultra-low latency systems
  • Shows knowledge of financial regulations and compliance

Key Focus Areas:

  • Ultra-low latency: Microsecond-level order processing
  • High throughput: Millions of orders per second
  • Order matching: Price-time priority algorithms
  • Market data integration: Real-time price feeds
  • Regulatory compliance: NMS, best execution, audit trails
  • Fault tolerance: Zero downtime for trading systems

Expected Discussion Points:

  • Order lifecycle (submission → matching → execution → settlement)
  • Order routing to market makers/exchanges
  • Order persistence and recovery
  • Real-time order status updates
  • Order cancellation and modification
  • Market data feed integration
  • Audit logging for compliance

Technologies to Mention:

  • In-memory order matching engine
  • Message queues (Kafka) for order processing
  • Database (PostgreSQL) for order persistence
  • Redis for real-time order status
  • WebSocket for status updates

2. Design a Limit Order Book System

Why It’s Critical:

  • Fundamental to understanding trading systems
  • Tests data structure and algorithm knowledge
  • Critical for market data display

Key Focus Areas:

  • Data structures: Red-black trees, heaps for order book
  • Order matching: Price-time priority matching
  • High-frequency updates: Millions of updates per second
  • Memory efficiency: Efficient storage of order levels
  • Real-time updates: Incremental updates vs. snapshots

Expected Discussion Points:

  • Order book data structure design
  • Price-time priority matching algorithm
  • Order level aggregation (price levels)
  • Best bid/offer (BBO) calculation
  • Market depth visualization
  • Order book reconstruction from snapshots
  • Handling order book updates

Technologies to Mention:

  • In-memory data structures (red-black tree, heap)
  • Time-series database for order book history
  • Message queue for order book updates
  • WebSocket for real-time distribution

3. Design a Portfolio Management System

Why It’s Critical:

  • Core user-facing feature
  • Demonstrates real-time data aggregation
  • Tests understanding of financial calculations

Key Focus Areas:

  • Real-time valuation: Portfolio value updates
  • Position tracking: Accurate position management
  • P&L calculation: Real-time profit/loss
  • Data consistency: Consistent across multiple services
  • High read throughput: Millions of portfolio queries per day

Expected Discussion Points:

  • Real-time price updates integration
  • Position aggregation across accounts
  • Portfolio valuation calculation
  • Historical performance tracking
  • Multi-account support
  • Caching strategies for portfolio data
  • Data consistency across services

Technologies to Mention:

  • Database (PostgreSQL) for positions
  • Redis for real-time portfolio cache
  • Real-time price feed integration
  • Aggregation service for portfolio calculation
  • Time-series database for historical data

4. Design a Market Data Distribution System

Why It’s Critical:

  • Essential for real-time trading
  • Tests understanding of high-throughput systems
  • Demonstrates knowledge of data normalization

Key Focus Areas:

  • High-volume streams: Millions of messages per second
  • Low-latency distribution: Real-time data delivery
  • Data normalization: Multiple data sources
  • Multiple consumers: Different services consuming market data
  • Data quality: Validation and error handling

Expected Discussion Points:

  • Market data feed ingestion
  • Data normalization from multiple sources
  • Message queue design for distribution
  • Consumer scaling and load balancing
  • Data validation and quality checks
  • Historical data storage
  • Market data replay capabilities

Technologies to Mention:

  • Kafka for message streaming
  • Feed handlers for data ingestion
  • Normalization service
  • Distribution service
  • Time-series database for historical data

5. Design a Payment Processing System

Why It’s Critical:

  • Core financial transaction system
  • Demonstrates understanding of payment regulations
  • Tests reliability and consistency requirements

Key Focus Areas:

  • Transaction reliability: No lost transactions
  • Idempotency: Handle duplicate requests
  • Fraud detection: Real-time fraud checks
  • Regulatory compliance: AML, KYC requirements
  • Banking integration: ACH, wire transfers
  • Transaction reconciliation: Match transactions

Expected Discussion Points:

  • Payment gateway integration
  • Transaction processing pipeline
  • Idempotency keys
  • Fraud detection integration
  • Banking partner integration
  • Transaction status tracking
  • Reconciliation system
  • Audit logging for compliance

Technologies to Mention:

  • Database (PostgreSQL) for transaction storage
  • Message queue for async processing
  • Fraud detection service
  • Banking API integration
  • Reconciliation service
  • Audit logging system

Tier 2: Very Common Questions (High Priority) - 40-70% of Interviews

These questions appear frequently and test important backend concepts. Practice these thoroughly after mastering Tier 1.

6. Design a Risk Management System

Why It’s Important:

  • Critical for regulatory compliance
  • Demonstrates understanding of financial risk
  • Tests real-time calculation capabilities

Key Focus Areas:

  • Real-time risk checks: Low-latency risk calculations
  • Position limits: Per-user and system-wide limits
  • Exposure limits: Risk aggregation
  • Margin calculations: Real-time margin requirements
  • Risk alerts: Notification system

Expected Discussion Points:

  • Risk calculation engine
  • Position limit enforcement
  • Exposure aggregation
  • Margin calculation logic
  • Risk rule engine
  • Alert generation
  • Risk reporting

Technologies to Mention:

  • Risk calculation service
  • Position aggregator
  • Rule engine
  • Alert service
  • Database for risk limits

7. Design a Real-Time Price Update System

Why It’s Important:

  • Core to trading platform
  • Tests WebSocket and real-time systems
  • Demonstrates high-frequency data handling

Key Focus Areas:

  • Ultra-low latency: Millisecond-level updates
  • High-frequency updates: Thousands per second per symbol
  • WebSocket connections: Millions of concurrent connections
  • Price aggregation: Multiple price sources
  • Memory efficiency: Efficient price storage

Expected Discussion Points:

  • Price ingestion from multiple sources
  • Price aggregation logic
  • WebSocket connection management
  • Price caching strategy
  • Historical price storage
  • Connection scaling
  • Price change notifications

Technologies to Mention:

  • WebSocket server
  • Price aggregator service
  • Redis for price cache
  • Time-series database
  • Load balancer for WebSocket connections

8. Design a Real-Time Analytics System

Why It’s Important:

  • Demonstrates stream processing knowledge
  • Tests understanding of analytics at scale
  • Shows real-time data processing capabilities

Key Focus Areas:

  • Stream processing: Real-time event processing
  • High-volume streams: Millions of events per second
  • Low-latency aggregations: Real-time metric calculations
  • Complex analytics: Multi-dimensional analysis
  • Data retention: Historical analytics

Expected Discussion Points:

  • Stream processing architecture
  • Event ingestion pipeline
  • Real-time aggregation logic
  • Analytics database design
  • Dashboard service
  • Data retention policies
  • Query optimization

Technologies to Mention:

  • Kafka Streams or Flink
  • Analytics database (ClickHouse, Druid)
  • Aggregation service
  • Dashboard service
  • Data retention service

9. Design a Banking Integration System

Why It’s Important:

  • Essential for deposits/withdrawals
  • Tests third-party API integration
  • Demonstrates error handling and reliability

Key Focus Areas:

  • Third-party APIs: Plaid, ACH integration
  • Data synchronization: Account data sync
  • Error handling: Retries and error recovery
  • Rate limiting: API rate limit handling
  • Security: Encryption and data protection

Expected Discussion Points:

  • Banking API client design
  • Account verification flow
  • Data synchronization strategy
  • Error handling and retries
  • Rate limiting implementation
  • Security and encryption
  • Monitoring and alerting

Technologies to Mention:

  • Banking API clients
  • Account verification service
  • Data sync service
  • Retry mechanism
  • Security layer
  • Monitoring service

10. Design a Distributed Job Scheduler System

Why It’s Important:

  • Common backend infrastructure pattern
  • Tests distributed systems knowledge
  • Demonstrates fault tolerance design

Key Focus Areas:

  • Distributed scheduling: Multiple scheduler instances
  • Job dependencies: Dependency management
  • Fault tolerance: Job retry and recovery
  • Resource allocation: Worker node management
  • Audit trail: Compliance requirements

Expected Discussion Points:

  • Job scheduling architecture
  • Job dependency management
  • Worker node management
  • Job retry and failure handling
  • Resource allocation
  • Monitoring and logging
  • Audit trail for compliance

Technologies to Mention:

  • Job scheduler service
  • Message queue (Kafka)
  • Worker nodes
  • Database for job metadata
  • Monitoring service

See Also: Detailed Job Scheduler Design

Tier 3: Common Questions (Medium Priority) - 20-40% of Interviews

These questions test important but less frequently covered areas. Familiarize yourself with these concepts.

11. Design a Transaction Reconciliation System

Key Focus Areas:

  • Transaction matching across systems
  • Discrepancy detection
  • Automated reconciliation
  • Manual reconciliation workflows

Expected Discussion Points:

  • Transaction matching algorithms
  • Discrepancy detection logic
  • Automated resolution strategies
  • Reconciliation workflows
  • Reporting and alerting

12. Design a Time-Series Database System

Key Focus Areas:

  • Time-series data storage
  • Efficient time-range queries
  • Data compression
  • High write throughput

Expected Discussion Points:

  • Time-series data model
  • Partitioning strategies
  • Compression algorithms
  • Query optimization
  • Retention policies

13. Design a Distributed Cache System

Key Focus Areas:

  • Distributed caching architecture
  • Cache invalidation strategies
  • Cache consistency
  • Performance optimization

Expected Discussion Points:

  • Cache architecture (Redis cluster)
  • Cache invalidation mechanisms
  • Consistency models
  • Cache warming strategies
  • Performance monitoring

14. Design a Message Queue System

Key Focus Areas:

  • Message ordering guarantees
  • Message persistence
  • Consumer scaling
  • Dead letter queues

Expected Discussion Points:

  • Message broker selection (Kafka)
  • Producer/consumer design
  • Message ordering
  • Durability guarantees
  • Consumer scaling strategies

15. Design an Authentication and Authorization System

Key Focus Areas:

  • User authentication
  • Multi-factor authentication (MFA)
  • OAuth integration
  • Role-based access control (RBAC)

Expected Discussion Points:

  • Authentication flows
  • MFA implementation
  • OAuth integration
  • Session management
  • Authorization policies

16. Design an Audit Logging System

Key Focus Areas:

  • Immutable audit logs
  • Compliance reporting
  • Log search and querying
  • Long-term retention

Expected Discussion Points:

  • Immutable log storage
  • Log ingestion pipeline
  • Search and querying
  • Compliance reporting
  • Retention policies

Tier 4: Less Common Questions (Lower Priority) - 10-20% of Interviews

These questions may appear but are less frequent. Understand the concepts but don’t prioritize detailed practice.

17. Design a Notification System

Key Focus Areas:

  • Multi-channel notifications
  • Delivery reliability
  • User preferences
  • Rate limiting

18. Design a Financial Data Warehouse

Key Focus Areas:

  • OLAP queries
  • Data aggregation
  • ETL pipelines
  • Data partitioning

19. Design a User Data Service

Key Focus Areas:

  • User profile management
  • Data privacy compliance
  • High read throughput
  • Data encryption

20. Design a Transaction Log System

Key Focus Areas:

  • Immutable transaction logs
  • Transaction replay
  • Audit trail
  • Long-term retention

21. Design a Fraud Detection System

Key Focus Areas:

  • Real-time fraud detection
  • Machine learning integration
  • Risk scoring
  • Pattern recognition

22. Design a RESTful API Gateway

Key Focus Areas:

  • API routing
  • Rate limiting
  • Authentication
  • API versioning

23. Design a WebSocket Service

Key Focus Areas:

  • Connection management
  • Message broadcasting
  • Connection scaling
  • Heartbeat management

24. Design a Third-Party Integration System

Key Focus Areas:

  • Third-party API integration
  • Rate limiting
  • Error handling
  • Data transformation

Fintech-Specific Design Patterns

Pattern 1: Trading Systems

  • Order Management: Order lifecycle, matching, routing
  • Market Data: Real-time feeds, normalization, distribution
  • Risk Management: Real-time checks, limits, exposure
  • Execution: Order matching, routing, settlement

Pattern 2: Financial Data Systems

  • Time-Series Data: Prices, trades, metrics
  • Real-Time Processing: Stream processing, aggregations
  • Historical Data: Data warehousing, analytics
  • Data Consistency: ACID transactions, eventual consistency

Pattern 3: Compliance and Security

  • Audit Trails: Immutable logs, compliance reporting
  • Data Privacy: Encryption, GDPR/CCPA compliance
  • Authentication: MFA, OAuth, session management
  • Fraud Detection: Real-time detection, ML models

Pattern 4: Payment Systems

  • Transaction Processing: Idempotency, reliability
  • Reconciliation: Transaction matching, discrepancy detection
  • Banking Integration: ACH, wire transfers, API integration
  • Fraud Prevention: Detection, risk scoring

Key Backend Concepts to Master

Distributed Systems

  • Consistency Models: Strong consistency, eventual consistency
  • CAP Theorem: Trade-offs in distributed systems
  • Replication: Master-slave, multi-master, quorum-based
  • Sharding: Horizontal partitioning strategies
  • Load Balancing: Algorithms and strategies

Real-Time Systems

  • Low Latency: Microseconds for trading systems
  • High Throughput: Millions of messages per second
  • Stream Processing: Kafka, Kafka Streams, Flink
  • WebSockets: Real-time data distribution
  • Event Sourcing: Event-driven architectures

Data Systems

  • Databases: PostgreSQL, MySQL, NoSQL (Cassandra, MongoDB)
  • Time-Series: InfluxDB, TimescaleDB
  • Caching: Redis, Memcached
  • Message Queues: Kafka, RabbitMQ
  • Data Warehousing: Snowflake, Redshift

Financial Systems

  • Order Books: Price-time priority, matching algorithms
  • Market Data: Feed handlers, normalization, distribution
  • Risk Management: Real-time risk checks, position limits
  • Compliance: Audit trails, regulatory reporting
  • Payments: Transaction processing, reconciliation

Fintech-Specific Considerations

Scale Expectations

  • Users: Millions of users
  • Orders: Millions of orders per day
  • Market Data: Millions of messages per second
  • Real-Time: Microsecond latency requirements
  • Reliability: 99.99%+ uptime

Technology Stack

Fintech companies commonly use:

  • Languages: Python, Go, Java, C++
  • Databases: PostgreSQL, Redis, Cassandra
  • Message Queues: Kafka
  • Caching: Redis
  • Monitoring: Prometheus, Grafana
  • Infrastructure: AWS, Kubernetes

Fintech Products

  • Stock Trading: Equity trading platform
  • Options Trading: Options trading infrastructure
  • Crypto Trading: Cryptocurrency trading
  • Banking: Cash management, spending accounts
  • Market Data: Real-time quotes, charts

How to Use This List

Preparation Strategy

  1. Start with Tier 1 Questions
    • Master trading infrastructure questions
    • Understand order execution systems
    • Practice market data systems
  2. Practice Tier 2 Questions
    • Cover payment and banking systems
    • Understand real-time systems
    • Practice risk management
  3. Review Tier 3 & 4 Questions
    • Familiarize with backend infrastructure
    • Understand compliance requirements
    • Know when to apply them

Practice Approach

For each question, practice:

  1. Problem Navigation
    • Clarify fintech-specific requirements
    • Understand regulatory constraints
    • Consider latency requirements
    • Identify compliance needs
  2. Solution Design
    • Backend architecture
    • Real-time data processing
    • Database design
    • API design
  3. Technical Excellence
    • Detailed component design
    • Performance optimization
    • Reliability and fault tolerance
    • Trade-offs analysis
  4. Technical Communication
    • Explain backend architecture choices
    • Discuss fintech considerations
    • Address compliance requirements
    • Discuss performance optimizations

Interview Tips

  1. Fintech-Specific Considerations
    • Always consider regulatory compliance
    • Think about audit trails
    • Consider data privacy requirements
    • Understand trading regulations
  2. Performance First
    • Ultra-low latency for trading
    • High throughput for market data
    • Real-time processing
    • Efficient data structures
  3. Reliability Critical
    • Fault tolerance
    • Data consistency
    • Transaction integrity
    • System availability
  4. Show Fintech Expertise
    • Demonstrate understanding of trading systems
    • Show knowledge of financial regulations
    • Discuss compliance requirements
    • Address fintech-specific challenges

Conclusion

This list provides a comprehensive overview of backend system design questions for Senior Backend Software Engineer positions. Remember:

  • Focus on trading infrastructure (order execution, market data, risk management)
  • Master real-time systems (low latency, high throughput, stream processing)
  • Prioritize reliability (fault tolerance, data consistency, compliance)
  • Think at fintech scale (millions of users, ultra-low latency)
  • Show fintech expertise (regulatory compliance, trading systems, financial data)

Key Success Factors:

  1. Strong backend architecture knowledge
  2. Trading system expertise
  3. Real-time system design skills
  4. Compliance and security awareness
  5. Understanding of fintech regulations

Good luck with your backend system design interview preparation!