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.