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Databricks·Software Engineer·Technical Phone Screen·Senior

Senior
Apr 2026

Summary

Databricks data engineer interview that zeroed in on pipeline architecture decisions. Just one meaty design question but it required knowing the DLT product well enough to argue for or against it.

Questions Asked (1)

Q1

When would you choose Delta Live Tables over building a pipeline directly on Spark and Delta Lake, and what are the trade-offs of each approach?

System DesignTechnical Trade-offsData Modeling
Author's notes

This one exposed a gap in my thinking.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the core differences: DLT is a declarative framework for building reliable pipelines with built-in data quality, lineage, and orchestration, while Spark+Delta gives full control and flexibility. Then discuss when each is appropriate based on requirements like data quality, operational overhead, and team expertise. Finally, outline trade-offs in terms of development speed, maintainability, cost, and performance.

Pro tip: Emphasize that DLT is not just a wrapper but a paradigm shift towards declarative pipelines, and mention that you'd consider hybrid approaches where DLT handles core ETL and custom Spark jobs handle complex transformations.

1. Define the decision criteria

List factors such as data quality requirements, pipeline complexity, team skills, operational overhead, and cost. These will guide the choice.

2. Explain when to choose DLT

Highlight scenarios like needing built-in data quality checks, automatic lineage, simplified orchestration, and faster development for standard ETL workloads.

3. Explain when to choose Spark+Delta

Discuss cases requiring fine-grained control, custom optimizations, non-standard transformations, or when the team has deep Spark expertise and wants to avoid vendor lock-in.

4. Compare trade-offs

Contrast development speed, maintainability, debugging, cost, performance tuning, and flexibility between the two approaches.

5. Conclude with a balanced recommendation

Summarize that the choice depends on context, and mention that DLT is ideal for most batch and streaming ETL, while Spark+Delta is better for advanced or highly customized scenarios.

Key Points to Mention

  • DLT provides built-in data quality expectations and automatic lineage, reducing boilerplate code.
  • DLT simplifies pipeline orchestration and monitoring, but may abstract away low-level control.
  • Spark+Delta offers maximum flexibility and control, but requires manual implementation of quality checks, lineage, and orchestration.
  • DLT can lead to faster development and lower operational overhead, but may introduce vendor lock-in and higher cost for some workloads.
  • Performance tuning in DLT is limited compared to hand-optimized Spark jobs.
  • Consider hybrid approaches: use DLT for standard ETL and custom Spark for complex transformations.

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