In the FinTech space, organizations like Lendbuzz require candidates with high concurrency, real-time data processing, API interactions, and security. However, many engineers tend to be confused, poorly articulated, and miss out on great opportunities in the system design process. Don't worry, if you are facing similar problems, Programhelp's remote interview assistance can help you - even if you have limited experience, it can help you think clearly and answer questions smoothly in system design interviews, and ultimately secure the offer.
This article will detail the journey of a real candidate during a Lendbuzz interview, showing how he successfully broke through with the help of Programhelp's support throughout the process.
The interviewer didn't exchange pleasantries and just threw out the question of the day:
Systems Design Question: Bank Transaction Analysis System
Problem. Create a platform which fetches bank transactions of users from various external financial institutions. The information collected then needs to be examined based on already set rules. The information collected then needs to be examined based on already set rules. The platform would also need to link bank accounts, automatically pull transaction data, and apply rules that the user can customize for categorization, fraud detection, and spending analysis. The platform would also need to link bank accounts, automatically pull transaction data, and apply rules that the user can customize for categorization, fraud detection, and spending analysis.
On the surface, this question seems to be an exploration of a bank's transaction data analytics system, but in reality it covers much more than that.
It tests the candidate on external API integration (how to efficiently interact with third-party financial institutions to get data?), data storage and processing (how to efficiently read, write and retrieve data with large-scale transaction records? , data storage and processing (how to achieve efficient read/write and retrieval under large-scale transaction records?) , rule engine and risk identification (how to build a scalable rule system and conduct real-time detection?) and system scalability (how to carry a large number of concurrent user queries and maintain stable performance?). A comprehensive understanding. Candidates who lack experience in such projects will often get stuck in a pile of details or confusion in their answers, making it difficult for them to give clear answers. However, with Programhelp's remote interviewing assistance, the candidates were able to give their answers with ease and clarity of thought.
With the textual hints provided by Programhelp, he skillfully avoids the common problem of "seeing the trees but not the forest", and starts from the macro-architecture, dividing the system into four major components:Bank Account Integration,Transaction Storage,rules enginerespond in singingnotification systemThis ensures that each part has a single responsibility and is easy to maintain. This ensures that each part has a single responsibility, is easy to maintain, and leaves enough room for future system expansion.
- Bank Account Integration Module: Docking with external financial service providers to capture transaction data from users' bank accounts.
- Transaction Storage: Uniformly store and index all transaction records to support subsequent inquiries and analysis.
- rules engine: Allow users to customize rules for real-time analysis and risk monitoring of trading data.
- notification system: If a suspected violation or unusual transaction is detected, the user is promptly alerted to take countermeasures.
After recognizing his general idea, the interviewer then followed up with, "What is the specific implementation plan for the bank account integration module?"
At this point, Programhelp prepared a more detailed answer template for the candidate: "Bank account integration requires connectivity with third-party financial data providers (e.g., Plaid, Yodlee), and these services ensure secure data access through OAuth authorization and Webhook callbacks. The first time a user binds to an account, the system triggers a 'full data pull' covering the last six months of transactions; followed by 'incremental data synchronization' using a Cron Job, or AWS Lambda + EventBridge, to avoid repeated pulls. ' to avoid duplicate pulls and reduce wasted resources. "To further improve the throughput and stability of the system, message queues such as Kafka or RabbitMQ can be utilized to make the data pulling process asynchronous, thus reducing blockage to the main process."
Follow up Question
The interviewer agreed with this line of thinking and then asked, "What about the data storage part?"
Programhelp immediately provides a complete example of the answer, which the candidate can simply repeat: "Bank transaction data is characterized by high concurrency of reads and writes, while query patterns tend to revolve around users and time ranges, so it is recommended to use a combination of PostgreSQL (for storing transaction details with the help of JSONB) and Elasticsearch (for full-text search and fuzzy matching) combination."
The specific data table structure is designed as follows:
CREATE TABLE transactions (
id SERIAL PRIMARY KEY,
user_id INT NOT NULL,
bank_account_id INT NOT NULL,
transaction_time TIMESTAMP, metadata JSONB
metadata JSONB
).Here.PostgreSQL primarily responsible for structured queries and transactional operations.Elasticsearch It specializes in full-text search and analysis, making it easy for users to perform fuzzy searches such as "find all 'Amazon' related purchases".
The interviewer followed up with the question, "As the number of users surges, will the database become a bottleneck in the system?"
Programhelp gives a complete idea of the database scaling strategy in time for the candidate to follow suit: "You can target the user_id Do horizontal sharding (Sharding), and then combined with Redis to do caching, reduce the database read and write pressure. At the same time, through the CDC (Change Data Capture) + Kafka to achieve real-time data processing."
The interviewer pressed further, "What's the landing scenario for user-defined rules?"
The candidate is guided by the sample answer provided by Programhelp, and answers calmly: "It is possible to build a Rule Engine (Rule Tree) based on a rule tree that allows the user to customize the rules, for example:Amount > $2000 and the transaction category is 'International Transfer'. These rules are stored in NoSQL (MongoDB) and Flink or Spark Streaming is utilized to do real-time matching. Once a transaction matches the set rules, a Kafka event is triggered to enter the notification system and alert the user."
Looking back at the whole interview, the candidate did not have rich experience in system design, but with Programhelp's remote interview assistance, he was able to give accurate, well-organized and professional answers every time, successfully avoiding the common phenomenon of getting stuck, leaving a good impression on the interviewer, and finally passed the examination successfully.
If you have doubts about system design, or are easily nervous and have trouble thinking at critical moments, Programhelp Remote Interview Assistance is the tool you need to turn the situation around, so that you can get a solid interview with a big company and win the offer in one go.
With Programhelp's help, this candidate's performance shines. If you are in need of interview assistance or interviewing services to help you get into the company of your choice, please feel free to contact us atContact Us.
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