JP Morgan·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

May 2026

Summary

System design round at JP Morgan for a software engineer role, centered entirely on designing a shopping cart service. Pretty deep dive, they wanted both read and write paths covered in detail which I wasn't fully expecting.

Questions Asked (1)

Design a shopping cart service for a large e-commerce platform. Walk through the write path (adding, removing, and updating items including quantity and variants), how you'd handle persistence across sessions and devices, and idempotency. Then cover the read path including retrieving the cart, computing totals, applying coupons and discounts, and surfacing fresh stock and pricing data. Also discuss your data model, storage choices, caching strategy, consistency between cart and inventory or pricing systems, and how you'd scale for high-traffic users and seasonal spikes.

System DesignData ModelingTechnical Trade-offs

Author's notes

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then structure your answer around the write path, read path, data model, and scaling strategy. Emphasize idempotency, consistency, and trade-offs, especially for a financial-grade e-commerce platform like JP Morgan.

Pro tip: Show maturity by explicitly discussing trade-offs (e.g., consistency vs. availability, caching vs. freshness) and how you'd monitor and mitigate risks like overselling or stale pricing. Mention idempotency keys and optimistic concurrency control as practical techniques.

1. Clarify Requirements and Scale

Ask about expected traffic, peak loads, consistency requirements, and integration with inventory/pricing systems. Define functional and non-functional requirements.

2. Design Write Path

Detail how to handle add, remove, update operations with idempotency (e.g., using request IDs), optimistic locking, and persistence across sessions/devices via a centralized cart service.

3. Design Read Path

Explain retrieving the cart, computing totals with coupons/discounts, and fetching fresh stock/pricing. Discuss caching strategies and consistency mechanisms.

4. Data Model and Storage

Propose a schema (e.g., carts, cart_items, coupons) and choose storage (e.g., NoSQL for scalability, SQL for transactions). Discuss sharding and replication.

5. Scaling and Consistency

Address high traffic and seasonal spikes with horizontal scaling, caching, async processing, and consistency between cart, inventory, and pricing (e.g., event-driven updates, TTL caches).

Key Points to Mention

Idempotency: Use idempotency keys for write operations to prevent duplicate submissions.
Persistence: Store carts in a centralized datastore (e.g., Redis, DynamoDB) keyed by user ID, with session merging for anonymous users.
Consistency: Implement optimistic concurrency control (versioning) and eventual consistency with inventory/pricing via events or TTL caches.
Caching: Cache cart reads and pricing/stock data with appropriate TTLs; use write-through or write-behind for cart updates.
Scalability: Shard by user ID, use read replicas, and employ async processing for non-critical updates (e.g., analytics).
Trade-offs: Discuss CAP theorem implications, latency vs. consistency, and cost vs. performance.

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