Diagnose data quality and pipeline performance issues
Company: Databricks
Role: Data Engineer
Category: Behavioral & Leadership
Difficulty: medium
Interview Round: Technical Screen
# Diagnose data quality and pipeline performance issues
## Scenario
You are interviewing for a **Data Solutions Architect** role. A customer is using a cloud data platform (e.g., Databricks on AWS/Azure/GCP) and reports:
- **Data quality issues** (incorrect/missing/duplicated records, inconsistent definitions)
- **Performance issues** (slow ETL/ELT pipelines, long query times, high compute cost)
They ask: “We’re struggling with data quality and performance—how would you approach this?”
## Tasks
1. **Discovery & scoping:** What questions do you ask to clarify the problem and constraints?
2. **Define success:** What *metrics* would you use for (a) data quality and (b) performance/cost? Include primary metrics and guardrails.
3. **Diagnosis plan:** Describe a step-by-step approach to identify root causes (data sources, pipeline stages, storage layer, compute, governance).
4. **Solution proposal:** Propose concrete technical and process changes to:
- Improve data quality (validation, monitoring, ownership, SLAs)
- Improve performance (storage layout, compute configuration, pipeline design)
5. **Concept check:** Explain the differences between a **data lake** and a **data warehouse**, and where a **lakehouse** fits.
6. **Cloud considerations:** What cloud concepts commonly matter in these engagements (e.g., security/IAM, networking, storage, encryption, cost)?
## Deliverable
Provide a structured plan you could present to the customer (bullets are fine), including short-term mitigations and longer-term architecture/process recommendations.
### Clarifying Questions to Ask
- Clarify the role, scope, timeline, stakeholders, and what success looked like.
- Use a real example with enough context for the interviewer to evaluate your judgment.
- Separate your own actions from team actions and quantify the result when possible.
### What a Strong Answer Covers
- A concise STAR or STAR+Reflection story with a specific situation and clear stakes.
- Concrete actions, trade-offs, communication choices, and ownership of mistakes or risks.
- A measurable result and a reflection on what you would repeat or change.
- Answers to likely probes about conflict, ambiguity, prioritization, and follow-through.
### Follow-up Questions
- What would you do differently if the same situation happened again?
- How did you keep stakeholders aligned when priorities changed?
- What evidence shows that your actions changed the outcome?
Overview: Diagnose data quality and pipeline performance issues evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Read the full Databricks Data Engineer interview experience this question came from