Navigating SIG’s System Design Interview Process

SIG (Susquehanna International Group, LLP) is one of the top global trading firms that specializes in quantitative tradingQuantitive trading is the use of mathematical, machine learning, and statistical tools and algorithms to identify investment opportunities and management. , market making, and investment strategies. Founded in 1987, SIG leverages sophisticated technology and quantitative research to succeed in high-stakes financial environments. Their operations require high-performance systems due to the nature of their roles and the speed at which they must operate.

Here’s an overview of some of their core operations and why each demands highly optimized, real-time technology:

• Market making: In market making, SIG provides liquidity by continuously quoting bid and ask prices for various financial instruments like stocks and derivatives, aiming to profit from the bid-ask spread while maintaining market stability.

• Proprietary trading: SIG engages in proprietary trading by using its capital to identify and exploit market inefficiencies, capitalizing on arbitrageArbitrage involves buying and selling an asset at the same time across different markets to take advantage of small price discrepancies. opportunities across asset classes such as equities, fixed income, and commodities.

• Investment strategies and quantitative research: SIG’s investment strategies are driven by quantitative methods, utilizing statistical models and machine learning to forecast trends and inform trading decisions across strategies like statistical arbitrage and portfolio optimization.

Are you ready to tackle one of the most challenging System Design interviews in the trading world? This guide provides a detailed roadmap to navigate SIG’s System Design interview process and stand out as a top candidate.

What draws tech talent to SIG#

According to Comparablyhttps://www.comparably.com/companies/326450 and Prosplehttps://au.prosple.com/graduate-employers/susquehanna-international-group/reviews, tech talent is drawn to working at SIG for a range of reasons centered on professional growth, cutting-edge technology, and financial incentives. SIG offers a highly collaborative, intellectually stimulating environment where employees can work on complex trading and investment strategies, especially within quantitative research and high-frequency trading. According to SIG, their culture supports continuous learning with access to robust training programs and mentorship, enabling skill development in fields like trading, mathematics, infrastructure engineering, software engineering, and data analysis.

Opportunities and roles at SIG#

SIG offers various roles and opportunities to professionals of different skill levels. The following are some common skill levels that SIG hires for:

• Experienced hires: From computer science and other relevant fields, SIG seeks experienced professionals with specialized expertise in distributed systems, low-latency architecture, and finance-related technologies. SIG leverages experienced professionals to develop new and improve existing backend infrastructure, giving itself a technological edge to enhance trading capabilities.

• Students and graduates: SIG engages students and fresh grads in high-stakes projects directly impacting trading strategies and financial systems. Candidates are mentored by seasoned professionals to develop their technical skills after introducing them to the finance industry. The focus is on hands-on experience, which helps bridge the gap between academic knowledge and real-world applications.

• Interns and co-ops: SIG exposes students to the firm’s high-performance systems early during internships. Interns and co-op students are involved in real-time problem-solving and gain practical experience managing and optimizing trading systems.

SIG hiring process#

Like FAANG companies, the SIG interview process has possibly seven stepshttps://au.prosple.com/graduate-employers/susquehanna-international-group/reviews/recruitment, which can vary from case to case depending on the role you apply for. For example, for an intern, some steps might be relaxed. Following are the general steps in the SIG interview process:

1. Application review: SIG’s hiring process begins with a thorough application review to assess candidates’ backgrounds and skill fit for the role.

2. Quantitative assessment: Candidates complete a quantitative assessment (quant), including a finance or math screener and a basic aptitude test, which measures the mathematical and analytical skills essential for SIG’s technical roles.

3. Initial screening or phone interview: The phone interview focuses on understanding the candidate’s experience, motivation, and fit with SIG’s culture and expectations.

4. Online technical interview: In this stage, candidates solve coding problems similar to Coding Interview and LeetCode style problems that can be solved using any programming language such as Java, Python, C#, etc. These interviews test the candidate’s technical abilities and problem-solving skills.

5. Case study: SIG presents candidates with a case study simulating real-world tasks, allowing them to showcase how they would handle scenarios relevant to the role.

6. On-site interviews include:

• Technical interview: Tests deeper technical knowledge and problem-solving abilities in areas relevant to SIG’s projects.
  Based on 231 interviews, as reported by WSOhttps://www.wallstreetoasis.com/company/susquehanna-international-group/interview, the following chart shows the percentage of difficulty level of coding interview questions. It contains 5 categories, including very easy, easy, average, difficult, and very difficult coding interview questions asked during the interview:

• System Design interview: This interview focuses on designing scalable and efficient systems, which are especially critical for high-frequency trading and data processing roles.

• Behavioral interview: This is the final round of interviews, which evaluates cultural fit and assesses soft skills, such as teamwork, adaptability, and communication.

• An office tour: Candidates get a glimpse into SIG’s work environment, helping them envision life at the company.

7. In-office desk rotation: For trading and quantitative roles, candidates meet with team members from different offices, gaining a broader perspective on SIG’s operations and fostering cross-office connections.

Steps 6 and 7 are collectively called Super Day and are conducted on the same day. After all the above steps, the candidates are informed about the outcomes of the interviews after a couple of weeks, and successful candidates are presented with an offer.

SIG System Design interview#

SIG’s System Design interview is renowned for its depth and focus on real-world technical challenges. Here’s a breakdown of the process that top tech talent goes through:

1. Technical screening: This initial step usually covers basic technical concepts and system design fundamentals, such as system architecture, scalability, and general interview questions related to distributed systems.

2. In-depth system design interview: The core of the interview presents candidates with a complex, real-world design problem often tied to SIG’s operations, such as high-frequency trading platforms, real-time data processing, or large-scale storage solutions. Candidates need to demonstrate creativity, precision, and scalability in their solutions. This portion simulates the high-stakes environments in which SIG’s systems operate, pushing candidates to design for real-time performance under extreme data loads.

3. Detail-oriented discussion: The interviewer sometimes dives into details to test the depth of the candidate’s knowledge. For example, they can discuss consistency, availability, and partition tolerance tradeoffs. They can also ask the candidate to explain their reasoning for architecture choices and how they handle potential bottlenecks and different nonfunctional requirements. 

4. Final review: In some cases, SIG may wrap up with a review of the design and ask the candidate to consider alternative approaches or identify any overlooked aspects. This final stage assesses their ability to self-reflect and optimize qualities crucial for maintaining SIG’s high-performance, low-latency systems.

The following illustration depicts the technical interview process, including the System Design interview.

Tips to ace System Design interview at SIG#

Here are some tips to ace the System Design interview at SIG:

1. Focus on real-time systems: Understand the fundamentals of real-time systems, including handling low-latency requirements and quickly processing high volumes of data.

2. Scalability and fault tolerance: Design systems that can handle the load without degradation and continue operations during failures.

3. Optimize for performance: Optimize response times to ensure the system can meet the demands of real-time applications.

4. Understand different techniques for data consistency: Study various consistency models and how to implement data consistency across distributed systems.

5. Understand some real-world systems: Analyze the architecture of well-known systems to gain practical insights.

6. Understand System Design interviews: System Design interviews are unique in their own way. Learn more about acing them using this free System Design interview guide.

7. Attempt mock interviews: Take System Design mock interviews to improve your problem-solving speed and receive feedback on your approach.

Let’s focus on some systems that are considered important for the System Design interview at SIG:

System Design interview questions at SIG#

Due to the nature of their business, SIG often includes real-time systems in their System Design interview. Here are some commonly asked System Design interview questions at SIG:

1. Real-time data processing system

2. Market data aggregator

3. Distributed cache system

4. Risk management system

5. Order matching engine

6. Trade execution system

Let’s expand each problem in detail and present its high-level design and the system’s workflow.

1. Real-time data processing system#

Problem statement#

Design a system for high-frequency data streams, such as real-time stock market data processing, focusing on low latency and reliability.

Functional requirements#

• Data ingestion: The system should be able to receive data streams from several sources simultaneously.

• Data processing: The system should support stream processing, including filtering and aggregation.

• Data analysis: The system should be able to analyze real-time data, such as pattern detection and correlation, and run queries on live data.

• Data output: The system should support multiple output channels and different output formats.

Nonfunctional requirements#

• Low latency: To meet real-time processing needs, the system should ensure low-latency data processing, ideally under 100 ms.

• Reliability: The system should be fault tolerant and have mechanisms to prevent data loss during failures.

• Scalability: The system should adopt to increased load (increased data volumes) without performance degradation.

• Availability: The system should maintain high availability, ensuring it is operational most of the time.

• Security: The system must ensure data security at rest and in transit to comply with relevant regulations.

Note: The functional requirements vary based on the nature of the design problem. However, the nonfunctional requirements for all the realtime systems discussed in this blog are almost similar to those outlined above.

High-level system design and workflow#

The real-time data processing system’s workflow begins with data sources stored in external cloud storage like AWS S3 and Google Cloud Storage (GCS) and internal storage systems (TimescaleDB, Cassandra). The data loader, such as Apache Kafka Connect, ingests and transforms this data, then passes it to the processing layers, which consists of:

• The stream layer

• The batch layer

The stream layer consists of stream processing services such as Apache Kafka Streams and stream analysis services such as Apache Spark Streaming for real-time data processing and analytics. The batch layer, containing batch analysis services such as Apache Spark or Hadoop and raw data storage such as HDFS and S3, handles historical data analysis and heavy computations. Both layers feed into a serving and integration layer that aggregates and serves processed data to traders and external systems through various interfaces.

The following illustration demonstrates a high-level design of a market data processing system:

Note: To understand the working of real-time processing systems, checkout the newsfeed generation and publishing system in detail.

2. Market data aggregator#

Problem statement#

Design a system that aggregates data from multiple market sources and provides clients with a unified data feed. This involves handling high volumes of data (scalability) and ensuring quick dissemination (low latency).

Functional requirements#

• Data collection: The system must support real-time data collection of market data from various sources, including stock exchanges, financial news APIs, and third-party data providers.

• Data normalization: The system should normalize incoming data to ensure consistency in formats, units, and terminology across different data sources.

• Data processing: The system should perform data processing and aggregation, including calculating averages, highs/lows, volume, and other relevant metrics.

• Data visualization: The system should provide tools and a dashboard for visualizing market data through charts, graphs, reports, etc.

• Data distribution: The system should provide streaming real-time data to clients and should support multiple delivery protocols.

High-level system design and workflow#

The flow of the following system begins by collecting market data from various sources, including stock exchange APIs, third-party data providers, and WebSocket feeds. The data ingestion layer takes the data, where a Kafka cluster handles high-throughput message streaming, while Redis serves as a caching layer for deduplication and quick access to recent data. The data then flows to a controller, distributing workload across multiple worker nodes in the processing layer. These workers (live aggregators) process the incoming data in real time, performing tasks like normalization, aggregation, and applying business rules. Finally, the processed data is stored in the storage layer, which consists of specialized databases (SQL and NoSQL): time series databases for temporal market data, a rules and logs database for maintaining system rules and audit trails, and aggregated databases for storing processed and transformed data.

The following illustration presents a high-level design of a market data aggregator system:

Note: You might be interested in the following similar problems:

• Web crawler system

• Distributed search system

3. Distributed cache system#

Problem statement #

Create a distributed cache system to handle frequent reads and updates, ensuring data consistency across multiple nodes in a high-traffic trading environment.

Functional requirements#

• Insert data: A distributed cache system user must be able to insert an entry into the cache.

• Retrieve data: The user should be able to retrieve data corresponding to a specific key.

Note: This is a common System Design problem and we leave this one as a challenge for you. However, if you are interested in the detailed design of distributed cache, you may check it out in our flagship "Grokking the Modern System Design" course.

4. Risk management system#

Problem statement#

Design a real-time risk management system that can analyze large volumes of financial data to flag or halt risky trades.

Functional requirements#

• Real-time data ingestion: The system must ingest large volumes of financial data from multiple sources, including market data, trade data, and news feeds, in real-time.

• Risk assessment and rating: The system should assess and assign a risk score to each transaction or asset based on predefined risk parameters such as volatility, market conditions, and position size.

• Automated alerts and notifications: When risky trades or anomalies are detected, the system should automatically trigger alerts for relevant stakeholders.

• Trade-halt mechanism: The system should automatically halt or block trades flagged as highly risky.

• Automated audit: The system should maintain detailed logs of all flagged trades, alerts, and actions taken.

High-level system design and workflow#

In the high-level design, the data ingestion layer collects data from from various sources. This layer uses Kafka streams for reliable message streaming and Redis for caching recent data. The data is then fed to the risk analysis core, which acts as a central hub. The core consists of rules persistent layer, and a risk engine. The risk engine consists of three key services: a risk calculator for computing various risk metrics, stress models for scenario analysis, and a limit service for enforcing trading boundaries. In parallel, the rules persistent layer maintains three distinct rule sets: position rules for managing exposure limits, trading rules for order validation, and compliance rules for regulatory requirements. The processed data and analysis results are stored in the persistence layer, which includes separate databases for market data, trade data, and real-time positions.

The following illustration shows a high-level design of a risk management system for market data:

Note: You might be interested in a similar system, i.e., System Design of a monitoring system.

5. Order matching engine#

Problem statement#

Design a matching engine for a real-time trading platform where buy and sell orders are matched. Focusing on performance, concurrency, and fault tolerance.

Functional requirements#

• Order management: The system should manage orders, such as modification and cancellation.

• Partial fills: The system should also support partial fills, allowing partial order fulfillment.

• Order matching logic: The system should match buy and sell orders based on price-time priority or other specified matching algorithms (e.g., FIFO, pro-rata).

• Order book management: The system should maintain an order book to accurately track all outstanding buy and sell orders organized by price levels and order arrival time.

• Order types support: It should support different order types, such as market, limit, stop, OCO, etc.

High-level design and workflow#

The workflow of the order matching system begins with users (traders) submitting orders through the order gateway, which acts as an interface for validating and routing these requests. Orders include various types, such as market, limit, and stop orders, and may include modifications or cancellations. Once validated, the orders enter the order matching core, which is processed by the price-time priority matcher. This component implements the matching algorithm, ensuring that buy and sell orders are matched based on the specified criteria, such as price-time priority or FIFO. The order book manager maintains an updated order book, tracking all outstanding orders by price levels and arrival time.

The execution manager processes matched orders and handles scenarios like partial fills, where only a portion of an order is fulfilled if an exact match is unavailable. Updates to order status (e.g., filled, partially filled, or canceled) are communicated to the state management service for consistency. The trade manager then records finalized trades into the Redis cluster, ensuring quick access to trade data. This data is further stored in the persistence layer, which stores trade data, market data, and audit logs for long-term storage and compliance.

The following illustration demonstrates a high-level design of an order-matching engine:

6. Trade execution system#

Problem statement #

Develop a low-latency system that can execute trades automatically based on incoming data, focusing on reliability and fast decision-making.

Functional requirements#

• Market data ingestion and processing: The system should collect data from various feeds and assets and should be able to process data for normalization and validation.

• Order management: The system should manage pending orders and track the status of submitted and completed orders.

• Risk management: The system should manage risk while executing an order utilizing the risk management system.

• Trade logs and audit trail: The system should maintain detailed logs of all trades, signals, and decisions for auditing and post-trade analysis.

High-level design and workflow#

The system starts with traders using their client interfaces to submit orders, which are distributed across the system via a load balancer to ensure even distribution of traffic. These orders pass through an order gateway that standardizes the order format and provides the initial entry point into the trading system. The orders then flow into the trading core services, where the portfolio manager checks for available funds and position limits. The order matching system receives validated orders and processes them according to market rules, while the order book maintains the current state of all active orders. The retransmitter ensures reliable order processing by handling any failed transactions, and the price tracker continuously updates market prices. All matched trades and critical data are temporarily stored in a Redis cluster for fast access and real-time processing. In the end, the system stores all data in the persistent layer, which consists of separate databases designed for trade data (executed trades), market data (price movements), and audit logs (system events and user actions) for historical record-keeping purposes.

The following illustration demonstrates a high-level design of a trade execution system:

Note: You might be interested in a similar realtime system known as distributed task schedular.

Conclusion#

SIG’s core operations, such as market making, proprietary trading, and quantitative research, demand highly optimized, real-time technology solutions to thrive in the fast-paced world of finance. Their high-performance systems ensure low latency, scalability, and fault tolerance, essential for managing massive data flows and executing rapid trades in high-frequency markets. This operational setup draws tech talent eager to work with cutting-edge technology. For aspiring candidates, mastering real-time system design, scalability principles, and performance optimization is key to success in SIG’s tough System Design interviews. With the right preparation, you can excel in SIG’s rigorous System Design interview process and join their ranks as a tech leader in high-frequency trading.

What’s next?#

At Educative, we have the following courses that can help you to grok the System Design and System Design interview skills: