Skill path
How to become a Data Engineer
How to become a Data Engineer: skill roadmap with 5 core skills, degree usually required, courses and salary data. Updated weekly.
Education & experience requirements
- Education
- Most of these occupations require a four-year bachelor's degree, but some do not.
- Experience
- A considerable amount of work-related skill, knowledge, or experience is needed for these occupations.
- On-the-job training
- Employees in these occupations usually need several years of work-related experience, on-the-job training, and/or vocational training.
Foundations
Skills assumed by employers — the base to build on
- MathematicsFoundation
- StatisticsFoundation
Collecting and interpreting numerical data. Employers need it to draw conclusions from data. Learn through study and hands-on analysis of datasets.
- Linear AlgebraFoundation
Mathematics of vectors and matrices. Employers need it for data transformations and algorithms. Learn through courses and exercises.
- DatabasesFoundation
Systems for storing, querying, and managing data. Needed for reliable data retrieval. Learn through SQL and database design exercises.
Core skills
The skills that define the role today
- Data PipelinesHigh demand
Automated processes that move data between sources and destinations. Employers need reliable data flow for analytics. Learn by building simple pipelines with workflow tools.
- PythonHigh demand
A versatile programming language. Data professionals use it for analysis, automation, and building data-driven applications.
- Data ModelingHigh demand
Designing database structures to store data logically. Employers need efficient data storage and retrieval. Learn through design practice and study of schemas.
- SQLHigh demand
A language for managing and querying databases. Analysts and engineers use it to extract and analyze data from various sources.
- Data QualityHigh demand
Ensuring data is accurate and consistent. Employers need trustworthy data for decisions. Learn by profiling data and using validation techniques.
Tools in demand
Specific tools that appear in live postings
- HadoopIn demand
A framework for storing and processing large datasets across clusters. Employers need it to handle big data. Learn by setting up clusters and using its tools.
- Microsoft AzureIn demand
Microsoft's cloud computing services for building and running applications. Essential for cloud-based infrastructure. Learn through Azure documentation and free practice environments.
- SparkIn demand
An analytics engine for large-scale data processing. Employers need fast processing across systems. Learn through its documentation and hands-on projects.
- AWSIn demand
Amazon's cloud platform for computing, storage, and services. Employers use it for scalable infrastructure. Learn by experimenting with AWS free tier and following official documentation.
- MySQLIn demand
Open-source relational database. Needed to store and query data. Learn through SQL tutorials and database practice.
- Data LakeIn demand
A central repository to store raw data in various formats. Needed for flexible analytics and data science. Learn about storage systems and cloud-based data engineering.
- ETLIn demand
Processes to extract, transform, load data. Core for data pipelines. Learn via SQL and integration tools.