What Does a Data Engineer Do? Skills, Certifications, and Career Path

Data engineers are increasingly in demand as companies need robust software architectures to store and access data from day one.

In this article we explore what data engineers actually do, how they differ from data scientists, the key skills required, and how to become one.

What Do Data Engineers Do?

Data engineers create and maintain analytical infrastructure that supports most data‑related functions. They develop, build, maintain, and test big‑data architectures such as databases and processing systems, and design pipelines for modeling, mining, acquiring, and validating data sets. Mastery of scripting languages and data‑analysis tools is essential.

Data Engineer vs. Data Scientist

While the roles overlap, data engineers focus on building and maintaining data infrastructure, whereas data scientists interact with that infrastructure to perform statistical analysis, machine learning, and advanced modeling to turn raw data into actionable insights.

Key Skills for Data Engineers

Big‑data architecture tools and components

Deep knowledge of SQL and other database solutions (e.g., Cassandra, BigTable)

Experience with data warehouses and ETL tools (e.g., Redshift, StitchData, Segment)

Proficiency in Hadoop‑based analytics (HBase, Hive, MapReduce)

Programming skills in Python, Java, C/C++, Perl, Go, etc.

Understanding of machine‑learning concepts and basic statistical analysis

Familiarity with multiple operating systems such as Unix, Linux, and Solaris

How to Become a Data Engineer

A computer‑science degree provides a solid foundation, but certifications are highly valued. Notable certifications include Google Cloud Professional Data Engineer, IBM Certified Data Engineer – Big Data, Cloudera CCP Data Engineer, and Microsoft MCSE: Data Management & Analytics. Online platforms like Udemy, edX, DataCamp, and Galvanize also offer relevant courses.

Conclusion

While certifications and courses can help you enter the field, practical experience remains essential. Building a strong skill set across the areas above will position you for a successful career as a data engineer in the rapidly evolving big‑data landscape.