How to Become a Data Engineer

The more companies invest in AI, the more they need people who can make their data usable in the first place. Data Engineers build the pipelines, platforms and infrastructure that get data where it needs to go, making the role a critical part of everything from analytics to machine learning.

If you want to move into Data Engineering, SQL and programming are the foundations. You will also need to understand databases, data pipelines and at least one major cloud platform such as AWS, Azure or Google Cloud.

There are several ways to build that experience. Some Data Engineers start with a computer science or STEM degree. Others move across from software engineering, database administration or business intelligence, or develop their skills through certifications, apprenticeships and structured training.

Whichever route you take, employers want to see that you can work with real data, build reliable systems and understand how data moves through them.

What Does a Data Engineer Do?

Data Engineers build and maintain the systems that collect, move, transform and store data. Their work gives Data Scientists, analysts and other teams access to reliable, usable data.

Day to day, that can mean building data pipelines, managing databases, integrating different data sources and maintaining cloud infrastructure. As Data Engineers progress, they take greater responsibility for platform reliability, performance and architecture.

Our guide to What Does a Data Engineer Do? explores the role and its responsibilities in more detail.

Do You Need a Degree to Become a Data Engineer?

No. A degree is one route into Data Engineering, but it is not the only one.

Computer science, software engineering, mathematics, statistics and other technical degrees can provide useful foundations in programming, databases and computational thinking. They can also provide a direct route into graduate Data Engineering roles.

You can also build the required skills through technical training, certifications, portfolio projects and experience in adjacent roles such as software engineering, database administration or analytics.

Other routes combine technical learning with practical experience. In England, for example, the Level 5 Data Engineer apprenticeship combines paid employment with structured training and typically takes 24 months.

Core Skills You Need

Data Engineering covers a broad technical stack, but you do not need to learn every tool before you start applying for roles. Build strong foundations first, then add technologies as your experience develops.

Harnham’s 2024 US Data & AI Salary Guide identified Python, Snowflake, AWS, Airflow and SQL among the most-used technologies in the US Data Engineering market.

SQL

SQL is fundamental to Data Engineering. You will use it to query, transform and manage structured data, so it should be one of the first skills you develop.

Python and other programming languages

Python is widely used for data processing, automation and pipeline development. Scala and Java are also used in Data Engineering, particularly for large-scale processing systems.

Data pipelines and integration

Learn how data moves between systems. That includes ETL and ELT processes, APIs, data ingestion, transformation and orchestration tools such as Airflow. Understanding the complete journey from raw source to usable data is more valuable than knowing individual tools in isolation.

Databases

Build experience with relational databases such as PostgreSQL and MySQL, alongside an understanding of NoSQL systems such as MongoDB. You should know how data is structured, stored and retrieved efficiently.

Cloud platforms

AWS, Microsoft Azure and Google Cloud are central to modern Data Engineering. Start with one platform rather than trying to learn all three at once. The aim is to understand how data systems are built and operated in a cloud environment.

Data processing

Tools such as Apache Spark become increasingly useful when you start working with larger datasets and distributed processing. Kafka is also widely used for streaming data and event-driven systems.

Data modelling and warehouse design

Data Engineers need to structure data so other people and systems can use it reliably. Learn the principles behind data modelling, data warehouses and modern analytical architectures.

Technical ability is only part of the role. Data Engineers work closely with Data Scientists, analysts, software teams and business stakeholders, so you also need to understand requirements, explain technical decisions and build systems that meet real business needs.

Data Engineering Certifications to Consider

Certifications can strengthen your Data Engineering credentials, particularly when you are building expertise in a specific cloud platform. But choose them carefully. Not every certification is designed for someone entering the profession.

The AWS Certified Data Engineer – Associate focuses specifically on Data Engineering workloads, including implementing data pipelines and monitoring, troubleshooting and optimising their cost and performance. It makes most sense once you have built the technical foundations and gained some practical experience.

The Google Cloud Professional Data Engineer covers designing data processing systems, ingesting and processing data, storage and maintaining automated workloads. There are no formal prerequisites, although Google recommends three or more years of industry experience, including experience designing and managing solutions using Google Cloud.

Microsoft Certified: Fabric Data Engineer Associate covers data ingestion and transformation, analytics solutions and monitoring and optimisation within Microsoft Fabric.

For people earlier in their careers, Microsoft Certified: Azure Data Fundamentals provides a more accessible introduction to core data concepts and working with data in Azure.

A certification should validate skills rather than replace them. Employers will still want evidence that you can apply what you have learned, so combine formal credentials with projects and hands-on experience wherever possible.

Common Routes Into Data Engineering

There is no standard Data Engineer career path. Your best route depends on your existing experience and how you prefer to learn.

1. Degree route

A degree in computer science, software engineering, mathematics, statistics or another technical discipline can lead into a graduate or entry-level Data Engineering role. This gives you a broad technical foundation before you specialise.

2. Certification and portfolio route

If you do not have a relevant degree, you can build the core skills independently and use certifications to validate specific areas of expertise. Combine SQL, Python, databases and cloud skills with projects that demonstrate how you apply them.

3. Apprenticeship route

Apprenticeships combine structured learning with practical experience. In England, the Level 5 Data Engineer apprenticeship is specifically designed around the skills and responsibilities of the profession and typically takes 24 months.

4. Structured training route

Bootcamps, professional training programmes and career-change courses can provide a more intensive way to develop technical skills. Look closely at what each programme teaches and whether you will build projects that demonstrate those skills to employers.

5. Lateral move

You may already have some of the foundations for Data Engineering. Software Developers, Database Administrators, BI professionals and Data Analysts can have significant overlap with the role. Moving laterally can mean building on existing programming, database or data skills rather than starting again.

Step-by-Step: Building Your Path to Data Engineering

1. Build strong SQL and Python skills. Start with querying and transforming data in SQL, then use Python for data processing and automation.
2. Learn how databases work. Understand relational database fundamentals, then build familiarity with NoSQL systems and when each approach is appropriate.
3. Choose one cloud platform. Get hands-on with AWS, Azure or Google Cloud rather than spreading your learning across all three.
4. Learn to build data pipelines. Practise moving data from a source through ingestion, transformation and storage. Learn the principles behind ETL and ELT rather than focusing only on a particular tool.
5. Add data processing tools. Once your foundations are strong, start working with technologies such as Spark for larger-scale data processing.
6. Build complete projects. Move beyond isolated tutorials. Create projects that take data from ingestion through transformation and storage, and be ready to explain the technical decisions you made.
7. Consider a relevant certification. Choose one that matches your preferred cloud platform and experience level.
8. Start targeting roles. Look for junior, graduate or entry-level Data Engineering positions, or opportunities to move laterally from an adjacent technical role. Once you’re in the profession, progression comes from taking greater ownership of pipelines, platforms and eventually architecture.

How Long Does It Take to Become a Data Engineer?

There is no fixed timeline. How long it takes depends heavily on what you already know and the route you choose.

A degree route typically takes several years, while apprenticeships and structured training provide alternative paths. Someone moving from software engineering, database administration or another technical role may progress faster because they already have relevant programming, database or infrastructure skills.

For career changers starting without that background, the priority is building the technical foundations. Before targeting Data Engineering roles, you should be able to write SQL and Python, work with databases, build a functioning data pipeline and demonstrate hands-on experience with a cloud platform.

Summary and Key Takeaways

• There is no single route into Data Engineering. A relevant degree can provide a strong foundation, but certifications, apprenticeships, structured training and experience in adjacent technical roles can also provide a route into the profession.
• SQL, Python, databases, data pipelines and cloud platforms form the core technical foundation. Build these before trying to learn every tool in the Data Engineering ecosystem.
• Certifications work best alongside practical experience. Choose credentials that match your preferred platform and current skill level, then demonstrate those skills through real projects.
• Explore What Does a Data Engineer Do? for a closer look at the role, or How Much Does a Data Engineer Make? to understand salary and progression.

Frequently Asked Questions

Can you become a Data Engineer without a degree?

Yes. A relevant degree can provide useful technical foundations, but it is not the only route. Certifications, apprenticeships, structured training, portfolio projects and experience in adjacent technical roles can all help you develop the skills required for Data Engineering.

What certifications help you become a Data Engineer?

Cloud certifications from AWS, Microsoft and Google Cloud can help validate Data Engineering skills. Choose a certification suited to your experience level and preferred platform. Credentials are most valuable when combined with practical projects and hands-on technical experience.

Is Python or SQL more important for Data Engineering?

You need both, but SQL is often the more universal starting point because working with stored data is fundamental to the role. Python is important for automation, data processing and pipeline development. Strong Data Engineers become comfortable using both for different parts of the job.

How long does it take to become a Data Engineer?

There is no standard timeline. A degree route typically takes several years, while people moving from adjacent technical roles may progress faster because they already have relevant programming, database or infrastructure experience.