Data Engineer Interview Landscape
Data Engineering Role Types
The data engineering field has diversified into specialized roles. Understanding these distinctions helps you target the right positions and prepare accordingly.
Core Data Engineering Roles
Data Engineer (Classic)
The foundational role focused on building and maintaining data infrastructure.
| Responsibility | Tools | Interview Focus |
|---|---|---|
| ETL/ELT pipelines | Airflow, Spark, dbt | Pipeline design, SQL |
| Data warehousing | Snowflake, BigQuery, Redshift | Modeling, optimization |
| Data quality | Great Expectations, dbt tests | Testing strategies |
| Infrastructure | AWS, GCP, Azure | Cloud services |
Typical Questions:
- "Design a pipeline to process 1TB of daily log data"
- "How would you handle late-arriving data?"
Analytics Engineer
Bridge between data engineering and analytics, focusing on data transformation and modeling.
| Responsibility | Tools | Interview Focus |
|---|---|---|
| Data modeling | dbt, SQL | Dimensional modeling |
| Metrics definition | Looker, Tableau | Business logic |
| Data documentation | dbt docs, data catalogs | Communication |
| Stakeholder collaboration | N/A | Soft skills |
Typical Questions:
- "How would you model a customer 360 view?"
- "Design metrics for measuring user engagement"
Data Platform Engineer
Infrastructure-focused role building the platforms that data engineers use.
| Responsibility | Tools | Interview Focus |
|---|---|---|
| Platform development | Kubernetes, Terraform | Infrastructure as code |
| Self-service tooling | Internal platforms | System design |
| Scalability | Distributed systems | Architecture |
| Developer experience | APIs, SDKs | API design |
Typical Questions:
- "Design a self-service data platform for 100 teams"
- "How would you implement multi-tenancy?"
Emerging Specializations
ML/AI Data Engineer
Focused on data infrastructure for machine learning pipelines.
| Focus Area | Skills |
|---|---|
| Feature stores | Feast, Tecton |
| Training data pipelines | Kubeflow, MLflow |
| Data versioning | DVC, LakeFS |
| Model data requirements | Schema evolution |
Streaming Data Engineer
Specialized in real-time data processing.
| Focus Area | Skills |
|---|---|
| Stream processing | Kafka, Flink, Spark Streaming |
| Event-driven architecture | Event sourcing, CQRS |
| Low-latency systems | Performance optimization |
| Exactly-once semantics | Distributed systems |
Role Comparison Matrix
| Aspect | Data Engineer | Analytics Engineer | Platform Engineer |
|---|---|---|---|
| Primary Focus | Pipelines & infrastructure | Models & metrics | Platform & tooling |
| SQL Depth | Advanced | Expert | Intermediate |
| Coding | Python, Scala | SQL, Python (light) | Python, Go, Java |
| System Design | Medium | Low | High |
| Business Context | Medium | High | Low |
| Compensation | $150K-$280K | $140K-$250K | $170K-$320K |
Choosing Your Target Role
Consider these factors when targeting positions:
-
Technical Depth vs. Breadth
- Data Engineer: Broad technical skills
- Platform Engineer: Deep infrastructure expertise
- Analytics Engineer: Deep business domain knowledge
-
Company Stage Preference
- Startups: Generalist data engineer roles
- Scale-ups: Specialized roles emerging
- Enterprise: Highly specialized positions
-
Career Trajectory
- IC Track: Staff/Principal Engineer
- Management: Engineering Manager → Director
- Specialist: Domain expert (streaming, ML infra)
Interview Insight: Tailor your preparation to the specific role. A Data Platform Engineer interview will have more system design; an Analytics Engineer interview will have more business case discussions.
Next, we'll explore the interview formats you'll encounter. :::