Data Engineer

Data Modeling and Design

Modeling Fundamentals:

• How do you decide between normalized and denormalized models for analytics use cases?
• Walk through how you would model customers, orders, and events for reporting.

Warehouse Design:

• What principles do you use when designing fact and dimension tables?
• How do you manage slowly changing dimensions in practice?

Data Contracts:

• What should be included in a data contract between producers and consumers?
• How do you handle schema evolution without breaking downstream workloads?

Pipeline Engineering

Batch and Streaming:

• When do you prefer batch processing versus streaming pipelines?
• Describe the key design choices for an event-driven ingestion pipeline.

Orchestration:

• How do you design DAGs that are observable, idempotent, and easy to recover?
• What is your approach to retry logic and dead-letter handling?

Performance:

• How do you diagnose and improve slow transformation jobs?
• What methods do you use to reduce warehouse compute cost while keeping SLA targets?

Data Quality and Reliability

Quality Checks:

• What layers of data quality checks do you typically implement?
• How do you detect and respond to silent data corruption?

Monitoring:

• What signals should trigger alerts for a critical data pipeline?
• How do you separate actionable alerts from noise?

Incident Response:

• Tell me about a data incident you handled and how you reduced recurrence risk.
• What should a strong data postmortem include?

Platform and Tooling

Tool Selection:

• How do you evaluate tradeoffs between managed and self-hosted data infrastructure?
• What criteria matter most when selecting orchestration and transformation tools?

Developer Experience:

• How do you improve local development and testing workflows for data engineers?
• How do you ensure pipeline changes are safely validated before production deploys?

Governance and Security:

• How do you implement access controls for sensitive datasets?
• What controls do you use for lineage, auditability, and compliance requirements?

Collaboration and Business Alignment

Stakeholder Partnership:

• How do you gather requirements from analysts, product teams, and data scientists?
• How do you push back when a request conflicts with platform standards or quality goals?

Prioritization:

• How do you prioritize foundational data platform work against urgent business asks?
• Describe a time you balanced short-term reporting needs with long-term architecture health.

Communication:

• How do you communicate data reliability and freshness expectations to non-engineering teams?
• What artifacts do you produce so consumers can trust and understand datasets?

Closing Questions

Role Fit:

• What kind of data platform environment helps you deliver your best work?
• How do you define success in your first 90 days?

Candidate Questions:

• What questions do you have about our data stack, governance model, or team structure?