Essential Skills Every Aspiring Data Engineer Needs
Data engineering is a complex field and you will need to have a wide array of tools in your toolkit to be able to excel. You have an important role in ensuring timely and accurate data insights for analysts, data scientists, and other stakeholders.
You will need a well rounded toolkit and a intuitive mind. Here are the essential skills to succeed as a data engineer:
Extensive SQL Knowledge
SQL has not changed a lot over the decades and you will be hard pressed to find an employer who doesn’t have a database already that you will either use as your main source (on-premises data warehouses, Snowflake, etc) or at least be a significant source of information (ERP, CRM, etc).
Having not only the basics down but more advanced topics like
using subqueries
CTE’s
temp tables
how to ensure performance and scalability
will help ensure your data pipelines complete in a timely fashion.
Understanding Data Models and Storage
You will no doubt run into various sources of media like videos, images, weather reports, pdfs and other file types, relational data. Knowing how to best store and handle this will require:
Familiarity with relational and non-relational database and storage systems
Knowledge of file formats like CSV, JSON, Avro, and Parquet
Archival strategies to ensure scalable history of pipeline runs and results
Programming Proficiency (Python Recommended)
Data engineering often requires programming skills, especially when direct database access is not possible or pipelines are getting complex. Python is an industry go to due to its extensive library ecosystem and ease of use. You’ll rely on it for:
Accessing data programmatically through REST APIs
Data cleaning and manipulation with libraries like Pandas and PySpark
Building ETL processes with Airflow
Unrelenting Thirst to Learn
Data engineering is constantly evolving, and the lines between roles like DBA, data engineer, analyst, and scientist are increasingly blurred and different at each employer. Staying modern requires:
Commitment to continuous learning
Adaptability to new tools and methodologies
Keeping up with trends like cloud computing, containerization, and orchestration
Problem-Solving and Debugging
You will run into various unexpected issues in your pipelines. Discovering if the problem lies in the source file, SQL server, blob storage, or data warehouse, a keen problem-solving mindset is essential. Strong debugging skills help you:
Identify the root cause of errors
Navigate the entire data pipeline stack
Implement long-term fixes to prevent recurring issues
Clear Communication Skills
As a data engineer you’ll interact with various teams including: developers, analysts, data scientists, managers, and business owners. Effective communication involves:
Translating technical jargon into plain language
Juggling between technical and business perspectives
Collaborating across departments to achieve shared goals
This will help ensure you can you deliver accurate and required results with the outputs of your data pipelines.
Things to Learn in 2025
Staying relevant in data engineering means keeping an eye on emerging trends. Here are areas worth exploring:
AI and Prompt Engineering: AI is reshaping IT fields, and understanding its applications will be crucial.
Snowflake/Data Bricks: you’ll be hard pressed to find positions without one of these in the job description
Programming and SQL: Master Python and a flavor of SQL like T-SQL.
Conclusion
Data engineering is an ever evolving and changing field and staying up to date on technologies is critical, as well as being able to have top tier technical skills for SQL and programming to ensure you have robust pipelines you will also need superb communication skills as you will be coordinating and working with various departments with different skills and knowledge.