Databricks·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Databricks data engineer interview with a scheduling/dependency scenario question. Pretty focused on practical pipeline reliability rather than anything algorithmic.

Questions Asked (1)

You have two nightly jobs where the second depends on the first. The first is scheduled at midnight and usually finishes in 20 minutes, so the second is set to kick off at 12:30 AM. But sometimes the first job runs long and the second fails. How do you fix this?

System DesignTechnical Trade-offsRoot Cause Analysis

Author's notes

My first instinct was to just say 'add a buffer, schedule it at 1 AM' which is obviously wrong because you're just pushing the same problem further down the road.

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AI HintsAI Generated

Suggested Approach

Start by diagnosing the root cause: the second job is scheduled by time, not by the first job's completion. Then propose a dependency-based trigger (e.g., event-driven or workflow orchestration) as the primary fix, and discuss trade-offs and alternatives like retries or monitoring.

Pro tip: Mention that simply increasing the buffer or adding retries is a band-aid; the real fix is to make the dependency explicit. Also, consider idempotency and failure handling to show production maturity.

1. Identify the root cause

Explain that the second job fails because it assumes the first job finishes by 12:30 AM, but the first job's runtime is variable. The scheduling is time-based, not dependency-based.

2. Propose a dependency-based trigger

Suggest using a workflow orchestrator (e.g., Apache Airflow, Databricks Jobs, AWS Step Functions) to trigger the second job only after the first succeeds. This eliminates the race condition.

3. Discuss implementation options

If orchestration isn't available, consider alternatives: have the first job emit a completion event that triggers the second, or use a polling mechanism with a lock file or database flag.

4. Address failure handling and monitoring

Add retries, alerting, and idempotency to handle transient failures. Ensure the second job can safely re-run if triggered multiple times.

5. Evaluate trade-offs

Compare solutions: orchestration adds complexity but is robust; event-driven is decoupled but requires infrastructure; polling is simple but may introduce latency. Choose based on team and system constraints.

Key Points to Mention

Root cause: time-based scheduling vs. dependency-based scheduling
Workflow orchestration tools (Airflow, Databricks Jobs, Step Functions)
Event-driven triggers (e.g., S3 event, message queue)
Idempotency and retry logic for the second job
Monitoring and alerting for job failures
Trade-offs: complexity, latency, cost, and maintainability

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