Barr Moses and the Monte Carlo team named the five pillars of data observability: freshness, distrib
A medium Pipeline Design mock interview question on DataDriven. Practice with AI-powered feedback, real code execution, and a hire/no-hire decision.
- Domain
- Pipeline Design
- Difficulty
- medium
Interview Prompt
Barr Moses and the Monte Carlo team named the five pillars of data observability: freshness, distribution, volume, schema, and lineage. The pillars are not a checklist; they are a diagnostic framework. When a consumer reports a wrong number, a senior engineer walks the pillars in order and uses each to narrow or rule out a class of cause. Most quality programs cover four pillars well and lineage poorly because lineage at column granularity is expensive to keep current. Audit pillar coverage by adding five monitor nodes on the curated table, one per pillar, plus a catalog node that captures the lineage pillar (which upstream column produced which output column).
How This Interview Works
- Read the vague prompt (just like a real interview)
- Ask clarifying questions to the AI interviewer
- Write your pipeline design solution with real code execution
- Get instant feedback and a hire/no-hire decision