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Perplexity·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say
Apr 2026

Summary

Perplexity system design round for a software engineer role. Just one question but it took up the whole session, which I wasn't fully expecting.

Questions Asked (1)

Q1

Design a personal finance management app similar to Mint.

System DesignData ModelingAPI & Integrations
Author's notes

I started with data ingestion from bank feeds and got pretty deep into the plumbing before realizing I hadn't touched the user-facing side at all.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scope, then design a high-level architecture that covers data ingestion, storage, and user-facing features. Focus on data modeling for transactions and accounts, and discuss API integrations with financial institutions, emphasizing security and scalability.

Pro tip: Demonstrate awareness of real-world constraints like aggregator APIs (Plaid), data privacy regulations (GDPR, CCPA), and the need for idempotent data sync. This shows maturity beyond just technical design.

1. Clarify Requirements

Ask questions to understand scope: target users, key features (budgeting, categorization, alerts), scale, and non-functional requirements like security and compliance.

2. High-Level Architecture

Outline main components: client apps, API gateway, services for accounts, transactions, budgeting, and a data pipeline for syncing with external financial institutions.

3. Data Modeling

Design core entities: User, Account, Transaction, Category, Budget. Discuss relationships, indexing for query performance, and handling of time-series data.

4. API & Integrations

Detail integration with aggregators like Plaid for account linking and transaction fetching. Discuss webhooks, polling, error handling, and rate limiting.

5. Scalability & Security

Address scaling reads/writes, caching, and security measures: encryption at rest/in transit, tokenization, and compliance with financial regulations.

Key Points to Mention

  • Use of third-party aggregators (e.g., Plaid, Yodlee) for bank connections and transaction data
  • Data model for transactions: amount, date, merchant, category, and support for splits/transfers
  • Categorization engine: rule-based and ML approaches for auto-categorizing transactions
  • Security: encryption, OAuth for third-party access, and compliance (PCI DSS, GDPR)
  • Scalability: sharding by user, read replicas, and caching frequently accessed data
  • Idempotency and consistency in data sync to avoid duplicates and ensure accuracy

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.