HIRE DATA ENGINEERS
Find Data Engineers Who Build Pipelines That Scale
Evaluate pipeline architecture thinking, data modeling decisions, and reliability mindset with structured assessments for data engineering hiring.
The Hiring Challenge
Data engineers build the infrastructure that makes data-driven decisions possible. A great data engineer builds pipelines that are reliable, scalable, and maintainable. A poor one creates data swamps that nobody trusts.
The problem: data engineering sits at the intersection of software engineering and data science. Traditional coding tests miss the data modeling and pipeline thinking. Data science interviews miss the engineering rigor.
Common Hiring Mistakes
Testing SQL tricks, not data modeling
Complex queries don’t tell you if they can design a data warehouse.
Ignoring reliability thinking
Pipelines that work 99% of the time are broken pipelines.
Skipping stakeholder communication
Data engineers serve analysts, scientists, and business teams. Communication matters.
Overweighting tool knowledge
Spark, Airflow, dbt are tools. Data modeling and pipeline design are skills.
Evaluation Framework
What LayersRank Evaluates
Technical Dimension
45%Pipeline Architecture
- ETL/ELT design decisions
- Batch vs. streaming trade-offs
- Orchestration and scheduling strategy
Data Modeling
- Dimensional modeling (star, snowflake)
- Schema design for analytical workloads
- Handling slowly changing dimensions
Data Quality
- Data validation strategy
- Testing data pipelines
- Monitoring and alerting for data issues
Behavioral Dimension
35%Stakeholder Communication
- Understanding analyst and scientist needs
- Translating business requirements to data models
- Managing expectations on data availability
Reliability Mindset
- Proactive monitoring
- Incident response for data issues
- Documentation of data lineage
Collaboration
- Working with data scientists on feature stores
- Coordinating with backend teams on data contracts
- Cross-team data governance
Contextual Dimension
20%Domain Understanding
- Interest in the business domain
- Data privacy and compliance awareness
- Cost optimization for data infrastructure
Sample Questions
Sample Assessment Questions
Design a data pipeline that ingests data from 5 different sources (2 APIs, 2 databases, 1 file upload) into a unified analytics layer. Walk me through your approach.
What this reveals: Pipeline architecture thinking, handling of heterogeneous sources, orchestration strategy.
An analyst reports that yesterday’s numbers don’t match the source system. Walk me through how you investigate.
What this reveals: Data debugging methodology, data lineage awareness, systematic problem-solving.
When would you choose batch processing over streaming? Give me a specific scenario for each.
What this reveals: Understanding of processing paradigms, practical trade-off reasoning, cost awareness.
Tell me about a time an analyst or data scientist asked you to build something you thought was wrong. How did you handle it?
What this reveals: Stakeholder management, ability to push back constructively, communication skills.
Describe a data quality issue you discovered and fixed. How did you prevent it from happening again?
What this reveals: Proactive quality mindset, systematic prevention thinking, documentation practices.
Evaluation Criteria
What separates strong candidates from weak ones across each competency.
Pipeline Design
Data Modeling
Data Quality
Communication
Reliability
How It Works
Configure your data engineering assessment
Use our template or customize for your data stack
Invite candidates
They complete the assessment async (35-45 min)
Review reports
See scores with confidence intervals across all dimensions
Make better decisions
Know exactly where to probe in final rounds
Time to first assessment: under 10 minutes
Pricing
| Plan | Per Assessment | Best For |
|---|---|---|
| Starter | ₹2,500 | Hiring 1-5 data engineers |
| Growth | ₹1,800 | Hiring 5-20 data engineers |
| Enterprise | Custom | Hiring 20+ data engineers |
Start Free Trial — 5 assessments included
Frequently Asked Questions
How long does the data engineering assessment take?
35-45 minutes. Covers pipeline design, data modeling scenarios, and behavioral questions.
Does it test specific tools (Spark, Airflow, dbt)?
The default assessment is tool-agnostic. You can customize to include questions about your specific data stack.
Can it assess analytics engineers too?
Yes. Analytics engineering shares many competencies. Adjust weights to emphasize modeling and SQL proficiency.
Can we see the questions before inviting candidates?
Yes. Full preview available after signup.
Ready to Hire Better?
5 assessments free. No credit card. See the difference structured evaluation makes.