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Databricks·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Interviewed for a data engineer role at Databricks and got asked about foundational data architecture concepts. Short post, just the one question worth sharing.

Questions Asked (1)

Q1

Can you explain what a Medallion Architecture is?

System DesignData ModelingTechnical Trade-offs
Author's notes

Pretty standard for a Databricks interview honestly.

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AI HintsAI Generated

Suggested Approach

Start by defining Medallion Architecture as a multi-layered data design pattern (Bronze, Silver, Gold) that progressively refines data quality. Then, explain each layer's purpose and how they enable scalable, reliable data pipelines. Finally, connect it to Databricks' Lakehouse platform and discuss trade-offs like cost vs. performance.

Pro tip: Emphasize that the Medallion Architecture is not just about layers but about enabling data reliability and self-service analytics; mention how it aligns with Delta Lake's ACID transactions and time travel for data quality.

1. Define the concept

State that Medallion Architecture is a data design pattern used to logically organize data in a lakehouse, with three layers: Bronze (raw), Silver (refined), and Gold (curated).

2. Explain each layer

Describe Bronze as raw ingested data, Silver as cleaned and conformed data, and Gold as business-level aggregates for reporting and ML.

3. Highlight benefits

Discuss how this architecture improves data quality, enables incremental processing, and supports diverse workloads from BI to advanced analytics.

4. Connect to Databricks

Explain how Databricks Lakehouse Platform with Delta Lake natively supports Medallion Architecture, providing ACID transactions, schema enforcement, and unified governance.

5. Discuss trade-offs

Mention considerations like storage cost, latency, and complexity, and how to optimize by choosing appropriate file formats and partitioning.

Key Points to Mention

  • Bronze, Silver, Gold layers and their purposes
  • Delta Lake's role in enabling ACID transactions and time travel
  • Incremental data processing and data quality enforcement
  • Support for both batch and streaming workloads
  • Unified governance with Unity Catalog
  • Trade-offs between cost, performance, and data freshness

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