anchorage digital·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026

Summary

Interviewed at Anchorage Digital for a backend or software engineering role. The technical round was basically one meaty design problem about building a leaderboard, which sounds straightforward but has a lot of moving parts once they start asking about complexity and real-world scale.

Questions Asked (1)

Design a leaderboard system that supports score updates, top-k retrieval, element deletion, and rank lookup. Walk through the data structures you'd use, time and space complexity, and any real-world optimizations.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

I went with a sorted set approach pretty quickly, something like a balanced BST or a skip list under the hood, which covers most operations in O(log n).

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (e.g., scale, update frequency, consistency needs) and then propose a hybrid data structure combining a hash map for O(1) score lookups and a balanced BST or skip list for ordered operations. Walk through each operation's time and space complexity, and discuss real-world optimizations like sharding, caching, and approximate ranking for massive scale.

Pro tip: Emphasize the trade-off between exact and approximate rankings: for very large leaderboards, approximate methods (e.g., sampling or bucketing) can drastically reduce memory and latency while maintaining user satisfaction. Also, mention that in financial systems like Anchorage Digital, auditability and consistency are critical, so consider persistent storage and transactional updates.

1. Clarify Requirements and Constraints

Ask about expected scale (number of users, updates per second), latency requirements, consistency needs, and whether rankings must be exact. This shapes the choice of data structures and optimizations.

2. Propose Core Data Structures

Suggest a hash map (user ID -> score) for O(1) updates and lookups, and a balanced BST (e.g., red-black tree) or skip list to maintain sorted order for top-k and rank queries. Explain how to keep them in sync.

3. Analyze Operations and Complexity

For each operation (update, top-k, delete, rank), derive time and space complexity. For example, update is O(log n) with BST, top-k is O(k log n) or O(k) with augmented trees, rank is O(log n) with subtree sizes.

4. Discuss Real-World Optimizations

Cover sharding by score range or user ID, caching top-k results, using approximate ranking for huge datasets, and leveraging in-memory stores like Redis with sorted sets. Mention persistence and consistency trade-offs.

5. Summarize and Evaluate Trade-offs

Conclude by comparing the proposed solution to alternatives (e.g., heap for top-k only, database with indexes) and justify choices based on requirements. Highlight how the design meets Anchorage Digital's needs for reliability and performance.

Key Points to Mention

Hash map for O(1) score updates and lookups, but need auxiliary structure for ordering.
Balanced BST (e.g., red-black tree) or skip list for O(log n) rank and top-k queries, with subtree size augmentation for rank.
Time complexity: update O(log n), top-k O(k log n) or O(k) with optimized traversal, delete O(log n), rank O(log n).
Space complexity: O(n) for storing all entries, plus overhead for tree pointers.
Real-world optimizations: sharding, caching, approximate ranking (e.g., using histograms or sampling), and using Redis sorted sets for in-memory performance.
Consistency and persistence: consider write-ahead logs, periodic snapshots, and transactional updates for financial-grade reliability.

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