Datadog Staff Software Engineer Interview Questions

Behavioral Questions (15)

1. 1.Tell me about a time you built monitoring for a new service from the ground up. Walk me through your instrumentation strategy and what metrics you chose to track.

   easy~3 min

   What interviewers look for

   • Demonstrates thinking about metrics, logs, and traces as first-class concerns rather than afterthoughts, aligning with Datadog's Observability First principle
   • Shows proactive instrumentation of business metrics and SLIs, not just infrastructure metrics
   • Mentions considering downstream consumers of the data and designing for discoverability

   Likely follow-ups

   • What alerts did you set up and how did you determine the thresholds?
   • How did you instrument for distributed tracing across service boundaries?

   Company context

   Datadog's Observability First principle expects engineers to instrument everything they build with the same rigor they expect from customers. Since Datadog's product is observability tooling, engineers must demonstrate deep intuition about what makes systems observable and debuggable in production.

2. 2.You're building a new feature that needs to query across all our time-series shards to aggregate metrics for the past week. Each shard holds about 10TB of data. How would you architect this without bringing down the cluster?

   easy~3 min

   What interviewers look for

   • Discusses fan-out query patterns and avoiding thundering herd problems when querying multiple shards simultaneously
   • Mentions pre-aggregation strategies or materialized views to avoid scanning raw time-series data for week-long queries
   • Considers caching layers, query timeouts, and circuit breakers to protect the cluster from expensive queries

   Likely follow-ups

   • What if the product team wants this query to run in real-time on dashboards with auto-refresh?
   • How would you handle the case where one shard is significantly slower than others?

   Company context

   Datadog's custom time-series database handles trillions of data points per day across thousands of shards. Engineers must understand distributed query patterns and protect production systems from expensive operations, especially given Datadog's 'Scale With Volume' principle and the need to maintain sub-second query performance for customer dashboards.

3. 3.Design a log parsing pipeline that can handle 100GB/hour of unstructured application logs and extract structured fields in real-time. The pipeline needs to support customer-defined parsing rules and route logs to different storage tiers based on content.

   easy~3 min

   What interviewers look for

   • Proposes streaming architecture with buffering and backpressure handling for consistent 100GB/hour throughput
   • Designs flexible parsing rule engine that customers can configure without service restarts (regex, grok patterns, or similar)
   • Implements intelligent routing logic based on parsed content - high-value logs to fast storage, archives to cold storage
   • Includes error handling for unparseable logs and monitoring of parsing success rates per customer

   Likely follow-ups

   • A customer pushes a parsing rule that causes 90% CPU usage on your pipeline workers. How do you protect the system?
   • How would you handle the case where a customer's log format changes suddenly, breaking their existing parsing rules?

   Company context

   Datadog's Log Management product ingests petabytes of log data across diverse customer environments. The company's 'Curious Builders' value expects engineers to design flexible systems that can adapt to customer needs without requiring engineering intervention, while 'Truth Through Data' means providing customers with reliable parsing and routing mechanisms.

4. 4.Tell me about a time you had to convince another team to adopt a shared technical standard or practice. How did you approach influencing them without any formal authority?

   easy~4 min

5. 5.We're seeing a 15% increase in metric ingestion volume month-over-month, but customer growth is only up 8%. Estimate where that extra volume is coming from and what it costs us operationally.

   easy~4 min

6. 6.Describe a system you designed that had to handle massive data volume or throughput. What were the bottlenecks you hit and how did you scale past them?

   medium~4 min

7. 7.A customer reports that their distributed traces are missing spans intermittently - about 5% of requests show incomplete traces. Walk me through how you'd debug this across our ingestion pipeline.

   medium~4 min

8. 8.We need to build a correlation engine that can link application errors in APM traces to infrastructure events happening on the same hosts. Design a system that can match traces to host metrics within a 5-minute window across our entire customer base.

   medium~4 min

9. 9.You're leading a cross-functional initiative that involves engineers from three different product teams plus infrastructure. How do you structure the work and communication to keep everyone aligned?

   medium~5 min

10. 10.A major enterprise customer reports their dashboards are loading 3x slower than last month, but our global P95 latency looks normal. Walk me through how you'd investigate this without access to their specific account details initially.

    medium~5 min

11. 11.Tell me about the most complex production incident you've owned end-to-end. What was broken, how did you debug it, and what process changes came out of the post-mortem?

    hard~5 min

12. 12.We want to add a new data type to our metrics ingestion pipeline - histograms with configurable bucket boundaries. The pipeline currently handles 50M points per second. Design the changes needed from agent to storage, considering our multi-tenant architecture.

    hard~5 min

13. 13.Design a real-time alerting system that can evaluate millions of metric thresholds per second across our multi-tenant infrastructure. How would you ensure alerts fire within 30 seconds while handling tenant isolation and preventing noisy neighbor problems?

    hard~5 min

14. 14.Describe a situation where you had to rebuild trust within an engineering team that had been burned by previous technical decisions. What was your approach?

    hard~5 min

15. 15.Estimate the annual infrastructure cost if we decided to retain all ingested logs for 2 years instead of our current 30-day default. Consider both storage and query performance implications across our customer base.

    hard~5 min