Senior Software Engineer Interview Questions

Interview Process

System Design & Architecture

Senior-level system design focuses on real-world constraints, irreversible decisions, migration planning, and architectural trade-offs under uncertainty.

• Design a distributed cache layer for a high-traffic e-commerce platform serving 1 million concurrent users. How would you handle cache invalidation and failover?
• You're migrating a monolithic payment system to microservices. What are the biggest risks? How would you execute this incrementally?
• Design a real-time analytics system that ingests 100,000 events per second and serves queries with sub-second latency. What trade-offs are you making?

  Sample Answer Guidance: I'd use a dual-write strategy: Kafka for event ingestion feeding both a columnar warehouse (Clickhouse/Druid) for OLAP queries and a hot cache (Redis) for sub-second results on recent data. This trades off staleness (cache is delayed) and cost (dual-write complexity) for query latency. Query patterns matter here—if 80% of queries hit recent data, this is optimal; for historical analytics, query latency requirements would drop significantly.
• Your company is deciding between building a custom solution or buying a third-party platform for data pipeline orchestration. What criteria would you use to evaluate this decision?
• Design a search system that must handle 10 billion documents with sub-100ms query latency. How would you index, distribute, and serve results?

  Sample Answer Guidance: I'd shard documents by ID range across Elasticsearch clusters (e.g., 20 shards, each handling 500M docs), with replicas for failover and query distribution. For sub-100ms latency, I'd use pre-computed aggregations and denormalised fields to avoid multi-pass queries. The trade-off: we sacrifice index freshness (batch updates every 5 mins) and write throughput for query speed. If real-time indexing is critical, we'd move to a tiered approach with a recent-doc hot index (Redis/Lucene) queried first.
• You've discovered your database schema is a bottleneck for future growth. How would you redesign it with zero downtime migration?

  Sample Answer Guidance: I'd use a dual-write approach: application code writes to both old and new schemas simultaneously, with a backfill job copying historical data while handling conflicts. Reads remain on the old schema until the backfill catches up, then we switch reads atomically (via feature flag or database routing). We'd validate row counts and checksums before cutover. Risk: the dual-write window creates complexity; we'd limit this to weeks, not months. If the schema change is simple (adding columns), we might use online schema alteration tools (pt-online-schema-change) instead for lower risk.
• Design a multi-region system that prioritises data consistency and disaster recovery. What are the operational implications?

  Sample Answer Guidance: For strong consistency, I'd use synchronous replication (primary writes to replicas before acking), accepting latency trade-offs (100-200ms cross-region write latency). For disaster recovery, I'd maintain hot standby replicas with RTO < 1 min and RPO = 0 (no data loss). Operationally, this means: complex failover orchestration (detecting primary failure accurately), additional monitoring for replication lag, and expensive bandwidth (synchronous multi-region costs 2-3x single-region). If consistency could relax to eventual, we'd use asynchronous replication, dropping operational complexity significantly.
• Your legacy system has accumulated significant technical debt. How would you prioritise refactoring efforts? What metrics would guide your strategy?

Advanced Problem-Solving & Production Ownership

Senior engineers own system reliability, debug complex production issues, and lead incident response across cross-system dependencies.

• Walk us through a complex production incident you debugged. What was your debugging methodology?
• Describe a performance issue that was difficult to diagnose. How did you approach root cause analysis?

  Sample Answer Guidance: We had latency spikes every hour on our search API. CPU looked normal, but tail latency hit 5s. I instrumented query spans with detailed breakdowns and discovered GC pauses during memory-intensive aggregations. Root cause: a seasonal traffic pattern triggered a full GC during peak load. I fixed it by tuning heap size and implementing request batching to smooth memory allocation. Systematically, I added per-endpoint latency percentiles and GC monitoring dashboards to catch similar issues earlier, not just the symptoms.
• Tell us about a time your system's performance degraded unexpectedly. How would you have prevented it?
• How do you design systems to be debuggable? What observability patterns do you use?

  Sample Answer Guidance: I build observability from day one: structured logging (JSON with request IDs), distributed tracing (span-level latency breakdown), and metrics (latency percentiles, error rates, downstream dependency health). For debugging, I aim for span-level visibility—each service logs its processing time, dependencies queried, and data transformed. On-call should answer 'why is latency high?' without querying code. I also design for graceful degradation: when a cache fails, logs show the fallback path taken, not just an error. This visibility trades write latency (logging overhead) for faster recovery (fewer human investigations).
• Describe a situation where you had to make a critical decision during a major outage. What was your reasoning?

  Sample Answer Guidance: During a payment processing outage, we discovered a replication lag in our database—the replica was 2 hours behind. I had to choose: wait for the replica to catch up (lose payments), or fail traffic to a secondary region (risk double-charges). I chose the secondary region but turned off asynchronous billing syncs until the primary caught up, accepting temporary billing delay over data loss. The reasoning: we could refund accidental charges later, but couldn't recover lost payments. I communicated the decision and its trade-offs immediately, not after the incident.
• How do you approach post-mortems? Describe your process for turning incidents into systemic improvements.
• Walk us through designing a system for high availability and disaster recovery. What's your RTO/RPO strategy?

How Senior Software Engineers Are Evaluated