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

Senior

May 2026

Summary

System design round at Microsoft for a software engineering role. The whole thing was centered on one big problem about change-data capture and diff persistence, which sounds focused but actually sprawls in a lot of directions once you start pulling on the threads.

Questions Asked (1)

Design a service that listens for database change notifications, computes the before/after diff for each change, and stores those diffs durably for downstream consumers. Cover schema design, notification reliability, ordering and idempotency, diff computation, storage model, and scalability.

System DesignData ModelingTechnical Trade-offs

Author's notes

I started with the notification pipeline and that was probably the wrong call.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then propose a high-level architecture that captures database changes via CDC or triggers, processes them through a durable queue, computes diffs, and stores them in an append-only log. Dive into each component: schema design for diffs, reliability mechanisms, ordering and idempotency guarantees, and scalability considerations.

Pro tip: Emphasize the importance of idempotent processing and exactly-once semantics, and discuss how to handle schema evolution and large transactions without overwhelming the system.

1. Clarify Requirements and Scale

Ask about expected throughput, latency requirements, data volume, and downstream consumer needs to tailor the design.

2. High-Level Architecture

Propose a pipeline: change capture (CDC/triggers) -> durable queue -> diff computation -> durable storage -> consumer API.

3. Schema and Storage Design

Design a schema for diffs that includes metadata (timestamp, transaction ID, table, operation type) and the before/after values, and choose a storage system (e.g., append-only log, NoSQL, or relational) based on access patterns.

4. Reliability, Ordering, and Idempotency

Explain how to ensure no data loss (e.g., at-least-once delivery with idempotent writes), maintain order (e.g., per-key ordering), and handle duplicates.

5. Scalability and Trade-offs

Discuss partitioning, batching, backpressure, and trade-offs between consistency, latency, and cost.

Key Points to Mention

Change Data Capture (CDC) mechanisms like Debezium or database triggers, and their trade-offs
Durable queue (e.g., Kafka) for reliability and decoupling
Diff computation strategies: row-level vs. column-level, handling large transactions
Storage model: append-only log vs. mutable store, and schema for diffs
Ordering guarantees: per-table or per-key ordering, and how to achieve it
Idempotency: using unique transaction IDs and deduplication to handle retries

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