The questions you face will heavily depend on the specific team you are interviewing for, but they generally follow consistent themes. AKUNA CAPITAL uses these questions not just to test your knowledge, but to see how you react to constraints, ambiguity, and edge cases.

Coding and Algorithms

These questions test your ability to write efficient, bug-free code under time pressure. Interviewers will look closely at your understanding of time and space complexity.

• Write a function to merge overlapping time intervals representing trading sessions.
• Implement an LRU (Least Recently Used) cache to store the most frequently accessed historical stock prices.
• Given a stream of incoming trades, write a program to maintain and output the median trade price in real-time.
• How would you detect a cycle in a directed graph representing data pipeline dependencies?
• Write a Python generator that reads a massive file line-by-line to prevent memory exhaustion.

System Design and Architecture

These questions evaluate your high-level architectural thinking and your ability to design for scale, fault tolerance, and low latency.

• Design a real-time data ingestion pipeline for market tick data that processes millions of messages per second.
• How would you design a system to reliably aggregate end-of-day trading logs from hundreds of distributed trading servers?
• Compare and contrast using Kafka versus RabbitMQ for a critical order-routing data stream.
• Design a distributed rate limiter for an internal API serving historical market data to quants.
• Walk me through how you would migrate a massive on-premise relational database to a distributed NoSQL solution without downtime.

SQL and Data Processing

These questions focus on your ability to manipulate data, optimize queries, and design logical schemas.

• Write a SQL query using window functions to calculate the 30-day rolling average of trading volume for a specific asset.
• Explain the difference between a clustered and non-clustered index, and when you would use each.
• How would you design a database schema to handle a rapidly changing hierarchy of financial instruments and their derivatives?
• Describe a time you had to optimize a slow-running SQL query. What steps did you take?
• Write a script to identify and remove duplicate records from a massive dataset without loading the entire dataset into memory.

Data Infrastructure and System Design

This is arguably the most critical evaluation area for senior candidates. You must prove you can design systems that handle the unique scale and latency requirements of a proprietary trading firm.

Be ready to go over:

• Message Brokers and Streaming – Designing pipelines using Kafka, RabbitMQ, or similar technologies to handle real-time market data.
• Storage and Databases – Choosing between relational (PostgreSQL), NoSQL (Cassandra, MongoDB), and time-series databases (InfluxDB, kdb+) based on read/write patterns.
• Distributed Computing – Utilizing frameworks like Apache Spark or Hadoop for large-scale historical data processing.
• Advanced concepts (less common) – Hardware-level optimizations, network protocols (TCP/UDP/Multicast), and designing for microsecond-level latency.

Example questions or scenarios:

• "Design a system to ingest, store, and serve real-time options chain data to a team of quantitative researchers."
• "How would you architect a fault-tolerant pipeline that guarantees exactly-once processing for critical trade execution logs?"
• "Explain the tradeoffs between using a time-series database versus a columnar storage format like Parquet for historical backtesting data."

SQL and Data Modeling

Despite the rise of big data frameworks, SQL remains a fundamental tool for data engineering. You will be evaluated on your ability to write complex, highly optimized queries and design logical data models.

Be ready to go over:

• Complex Queries – Window functions, CTEs (Common Table Expressions), and complex joins.
• Query Optimization – Analyzing query execution plans, understanding indexing strategies, and avoiding full table scans.
• Schema Design – Designing normalized schemas for transactional systems and denormalized star/snowflake schemas for analytical workloads.
• Advanced concepts (less common) – Database internals, locking mechanisms, and handling transaction isolation levels.

Example questions or scenarios:

• "Given a table of order executions, write a query to find the top 5 most traded options contracts per day, partitioned by underlying asset."
• "Design a data model to track the historical changes of an options pricing model over time."
• "How would you optimize a slow-running query that joins a massive table of historical market data with a smaller table of corporate actions?"

Key Responsibilities

As a Data Engineer at AKUNA CAPITAL, your day-to-day work revolves around ensuring that the firm's data ecosystem is robust, scalable, and highly performant. You will spend a significant portion of your time designing and implementing automated data pipelines that extract raw market data from various exchanges, transform it into usable formats, and load it into centralized data lakes or time-series databases.

Collaboration is a massive part of the role. You will work side-by-side with quantitative researchers to understand their data requirements for backtesting new trading strategies. This often involves creating custom data views, optimizing their queries, or building APIs that serve historical data with minimal latency. You will also partner with core software engineers to ensure that the data infrastructure integrates seamlessly with the firm's proprietary trading systems.

Beyond building new pipelines, you will be responsible for the continuous optimization and monitoring of existing infrastructure. This includes identifying performance bottlenecks, scaling distributed compute clusters, and implementing rigorous data quality checks to ensure that no corrupted or delayed data impacts the firm's trading decisions. You will frequently prototype new open-source big data tools to see if they can provide a competitive edge in processing speed or storage efficiency.

Role Requirements & Qualifications

To thrive as a Data Engineer at AKUNA CAPITAL, you must possess a strong foundation in software engineering combined with a deep understanding of distributed systems. The firm values engineers who are pragmatic, detail-oriented, and capable of operating in a high-stakes environment.

• Must-have technical skills – Expert-level proficiency in Python or C++; advanced SQL capabilities; deep understanding of data structures and algorithms; experience with Linux/Unix environments.
• Must-have system skills – Proven experience designing and maintaining scalable data pipelines; familiarity with distributed messaging systems (e.g., Kafka) and relational databases (e.g., PostgreSQL).
• Nice-to-have technical skills – Experience with time-series databases (e.g., kdb+/q, InfluxDB); proficiency in big data processing frameworks (e.g., Apache Spark, Hadoop); familiarity with containerization (Docker, Kubernetes).
• Domain experience – While prior experience in finance or proprietary trading is a strong nice-to-have, it is rarely a strict requirement. However, an interest in financial markets and a willingness to learn options pricing concepts is highly valued.
• Soft skills – Strong analytical thinking; the ability to communicate complex technical tradeoffs clearly; a high degree of ownership and accountability for the systems you build.

Frequently Asked Questions

Q: Do I need a background in finance or trading to be successful in this interview? While a background in finance or options trading is advantageous, it is not strictly required for most Data Engineer roles at AKUNA CAPITAL. The interviewers are primarily evaluating your engineering fundamentals, problem-solving skills, and ability to build scalable infrastructure. If you lack financial knowledge, focus on demonstrating exceptional technical depth.

Q: How difficult is the technical phone screen? The technical phone screen is highly rigorous. You should expect complex algorithmic questions or hands-on data manipulation tasks that require you to write executable code. Preparation should involve extensive practice on platforms like LeetCode (Medium to Hard difficulty) and writing custom data parsing scripts from scratch.

Q: What is the culture like for engineers at AKUNA CAPITAL? The culture is fast-paced, collaborative, and highly meritocratic. Engineers work in close proximity to traders and quantitative researchers, meaning feedback loops are incredibly short. You will be expected to take ownership of your projects, communicate proactively, and thrive in an environment where technical excellence is the baseline.

Q: How long does the interview process typically take? From the initial online assessment to the final offer, the process usually takes between 3 to 6 weeks. The timeline can vary based on candidate availability and the specific team's urgency. Recruiter communication is generally prompt and transparent throughout the process.

Other General Tips

To maximize your chances of success during the AKUNA CAPITAL interview process, keep these strategic tips in mind:

• Clarify before coding: Never jump straight into writing code. Always take a moment to clarify the requirements, ask about edge cases (e.g., negative numbers, missing data, extreme volumes), and outline your proposed solution.
• Communicate tradeoffs explicitly: In system design rounds, there is rarely a single "correct" answer. Your interviewers want to hear you articulate the pros and cons of different approaches. Discuss latency versus throughput, consistency versus availability, and development speed versus system complexity.