# Data Engineer Jobs

Build the data foundation. Enable analytics and ML with reliable pipelines.

## What is a Data Engineer?

Data engineers build and maintain data pipelines, warehouses, and infrastructure. At startups, you'll enable data-driven decisions by ensuring clean, reliable, and accessible data for analytics, reporting, and machine learning.

## Salary Ranges

- **Junior (0-2 years):** $80k - $115k  
- **Mid-Level (2-5 years):** $115k - $170k  
- **Senior (5+ years):** $170k - $260k

## Typical Responsibilities

- Build and maintain ETL/ELT pipelines  
- Design and manage data warehouses  
- Ensure data quality and reliability  
- Optimize query performance  
- Support analytics and ML teams  
- Document data models and processes

### Required Skills

- SQL (advanced)  
- Python  
- ETL/ELT concepts  
- Data warehousing  
- Cloud platforms  
- Git version control

### Nice to Have

- Spark or similar  
- Airflow or similar orchestration  
- dbt  
- Streaming (Kafka, Kinesis)  
- Data modeling  
- Basic ML understanding

## Career Path

- **Entry:** Junior Data Engineer (0-2 years)  
- **Mid:** Data Engineer (2-5 years)  
- **Senior:** Senior Data Engineer (5-8 years)  
- **Staff:** Staff Data Engineer (8+ years)  
- **Lead:** Data Engineering Lead (6+ years)

## Interview Tips

### Know SQL Inside Out

Data engineering interviews often have complex SQL problems. Practice window functions, CTEs, and query optimization.

### Understand Data Modeling

Be ready to design schemas, discuss normalization vs denormalization, and explain dimensional modeling.

### Discuss Trade-offs

Batch vs streaming, SQL vs NoSQL, centralized vs distributed—be ready to discuss when to use what.

### Show Production Experience

Data engineering is about reliability. Discuss how you've handled data quality, monitoring, and failure scenarios.

## Profile Tips for Data Engineers

- Show data pipeline projects  
- Include SQL examples  
- Mention specific tools (Airflow, dbt)  
- Explain data modeling decisions  
- Describe scale and complexity handled

## Frequently Asked Questions

**What's the difference between data engineer and data scientist?**  
Data engineers build the infrastructure and pipelines; data scientists analyze and model the data. Engineers make data accessible and reliable; scientists extract insights from it. Both are essential.

**Is Python or SQL more important?**  
Both are essential, but SQL is often more critical day-to-day. Advanced SQL skills (window functions, optimization) are used constantly. Python is essential for pipeline code and more complex transformations.

**Do I need to know machine learning?**  
Not required, but helpful. Understanding basic ML concepts helps you support data scientists and build better pipelines for ML workloads. You don't need to be an ML expert.

**Is data engineering in demand?**  
Very much. Every company with data needs data engineering, and the talent pool is smaller than software engineering. It's one of the highest-demand and highest-paying engineering specialties.