DATA ENGINEER JOBS

Data Engineer Jobs

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

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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

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

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 projectsInclude SQL examplesMention specific tools (Airflow, dbt)Explain data modeling decisionsDescribe scale and complexity handled
FAQ

Frequently Asked Questions

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.

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.

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.

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.

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