Amazon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Amazon SWE coding round with a recursive schema validation problem. Pretty meaty for a single question, had to design the schema format myself and handle nested objects, arrays, and type checking all in one go.

Questions Asked (1)

Design and implement a schema validation system for JSON-like data. Given a data value and a schema definition you design yourself, return whether the data conforms to the schema. Must handle primitives, nested objects, and arrays recursively, with up to 100k total nodes.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The part that tripped me up first was that they let me design the schema format, which felt like a gift but actually added pressure.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and defining a simple, expressive schema DSL (e.g., type strings, objects with required/optional fields, arrays with item schemas). Then design a recursive validator that traverses the data and schema in tandem, using an explicit stack or recursion with depth limits to handle 100k nodes efficiently. Discuss trade-offs like strict vs. lenient validation, performance optimizations (e.g., short-circuiting, memoization), and error reporting.

Pro tip: Mention that you'd use an iterative approach with an explicit stack to avoid stack overflow on deeply nested data, and that you'd validate schema definitions upfront to catch errors early. This shows you think about robustness and real-world constraints beyond the happy path.

1. Clarify requirements and define schema DSL

Ask about supported types, strictness (e.g., additional properties allowed?), and error reporting needs. Propose a simple schema format: strings for primitives ('string', 'number'), objects with 'type' and 'properties', arrays with 'items', and optional 'required' flags.

2. Design recursive validation algorithm

Outline a function that takes (data, schema) and returns boolean or error list. Handle primitives by type check, objects by iterating schema properties and recursing, arrays by validating each element against 'items' schema. Use an explicit stack or recursion with depth limit to avoid stack overflow.

3. Address performance and scalability

Discuss time complexity O(n) where n is total nodes, and space O(d) for depth. Mention optimizations: short-circuit on first error, avoid unnecessary allocations, and consider iterative traversal for 100k nodes. Also note schema pre-compilation if reused.

4. Handle edge cases and error reporting

Cover null/missing values, type mismatches, extra properties, empty arrays/objects, and cyclic data (if possible). Decide whether to return boolean or detailed errors; if errors, design a structure with path and message.

5. Discuss trade-offs and extensions

Compare strict vs. lenient validation, recursive vs. iterative, and simple vs. feature-rich schema (e.g., unions, patterns). Mention how to extend for custom validators or async validation, and how to test with large datasets.

Key Points to Mention

Schema DSL design: primitives, objects with required/optional fields, arrays with item schemas, and possibly nested schemas.
Recursive validation with explicit stack or depth-limited recursion to handle 100k nodes without stack overflow.
Time and space complexity: O(n) time, O(d) space where d is depth; short-circuiting for early exit.
Error handling: return boolean or detailed error objects with path and message; consider collecting all errors vs. first error.
Performance optimizations: pre-compile schema, avoid unnecessary object creation, use iterative traversal for deep nesting.
Trade-offs: strict vs. lenient validation, support for additional properties, and extensibility for custom types.

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