Data Engineering has become one of the fastest-growing careers in technology. As businesses collect more data than ever before, companies need professionals who can build reliable systems that move, store and process that information.

Whether it’s an online bank, an iGaming company, a fintech startup or an international software business, almost every organisation relies on Data Engineers to ensure their data is accurate, available and ready for analysis.

If you’re considering a career in Data Engineering, this guide explains the skills you’ll need, the technologies employers are looking for and how to get started.

What Does a Data Engineer Do?

A Data Engineer designs and maintains the systems that collect, transform and store data.

Instead of analysing data, Data Engineers focus on building the infrastructure that allows Data Analysts, Business Intelligence teams and Data Scientists to work efficiently.

Typical responsibilities include:

• Building ETL and ELT pipelines
• Designing data warehouses
• Developing cloud data platforms
• Writing SQL queries
• Automating data processing
• Monitoring data quality
• Optimising performance
• Integrating APIs
• Managing large datasets

Think of a Data Engineer as the person who builds the roads that data travels on.

Skills Every Data Engineer Needs

Modern Data Engineering combines programming, databases and cloud computing.

The most important skills include:

SQL

SQL is the most important skill for Data Engineers.

You’ll use it every day to:

• Query databases
• Join tables
• Optimise performance
• Build reports
• Transform data

Strong SQL knowledge is essential for almost every Data Engineering role.

Python

Python has become the standard programming language for Data Engineering.

It’s commonly used to:

• Build ETL pipelines
• Process files
• Automate tasks
• Work with APIs
• Clean data

Popular Python libraries include:

• Pandas
• PySpark
• Requests

Cloud Platforms

Most companies now build data platforms in the cloud.

Popular cloud providers include:

• Microsoft Azure
• Amazon Web Services (AWS)
• Google Cloud Platform (GCP)

Understanding cloud storage, networking and managed data services will significantly improve your career opportunities.

Data Warehouses

Modern companies store data in specialised platforms.

Common technologies include:

• Snowflake
• BigQuery
• Azure Synapse Analytics
• Amazon Redshift

Understanding how data warehouses work is one of the core responsibilities of a Data Engineer.

ETL and ELT

ETL stands for:

• Extract
• Transform
• Load

ELT stands for:

• Extract
• Load
• Transform

These processes move data between different systems while ensuring it remains accurate and consistent.

Most Data Engineering jobs involve building or maintaining ETL pipelines.

Workflow Automation

Modern pipelines are usually automated.

Popular orchestration tools include:

• Apache Airflow
• Azure Data Factory
• AWS Glue
• Prefect
• Dagster

These tools schedule and monitor data pipelines.

Big Data

Not every company works with Big Data, but experience with distributed processing can be valuable.

Popular technologies include:

• Apache Spark
• Databricks
• Hadoop

Spark has become one of the most widely used technologies for processing large datasets.

Version Control

Like software developers, Data Engineers use Git to manage code.

Understanding:

• Git
• GitHub
• GitLab
• Azure DevOps

is now expected by most employers.

Which Industries Hire Data Engineers?

Data Engineers are needed in almost every industry.

Some of the largest employers include:

• FinTech
• Banking
• iGaming
• SaaS
• Telecommunications
• Healthcare
• Retail
• Artificial Intelligence

As businesses continue investing in analytics, demand for Data Engineers is expected to remain strong.

Do You Need a Computer Science Degree?

Not necessarily.

Many successful Data Engineers come from backgrounds such as:

• Software Development
• Business Intelligence
• Database Administration
• Mathematics
• Physics
• Engineering

Practical skills and commercial experience are usually more important than a specific degree.

Learning Roadmap

If you’re starting from scratch, a practical roadmap might look like this:

Step 1

Learn SQL thoroughly.

Practice:

• SELECT
• JOIN
• GROUP BY
• Window Functions
• Indexes

Step 2

Learn Python.

Focus on:

• Variables
• Functions
• APIs
• File processing
• Data manipulation

Step 3

Learn one cloud platform.

Choose:

• Azure
• AWS
• Google Cloud

Step 4

Build ETL projects.

For example:

• Import CSV files
• Connect to APIs
• Store data in SQL
• Automate workflows

Step 5

Learn a modern data warehouse.

Examples:

• Snowflake
• BigQuery
• Redshift
• Synapse

Step 6

Learn orchestration.

Start with:

• Airflow
• Azure Data Factory

Step 7

Learn Spark or Databricks.

Large companies increasingly use distributed data processing.

Certifications

Certifications aren’t essential but can strengthen your CV.

Popular options include:

• Microsoft Azure Data Engineer Associate
• AWS Certified Data Engineer – Associate
• Google Professional Data Engineer
• SnowPro Core Certification
• Databricks Certified Data Engineer Associate

Remember that real projects are usually more valuable than certificates alone.

Build Your Portfolio

A portfolio demonstrates practical skills.

Ideas include:

• Building an ETL pipeline
• Creating a data warehouse
• Processing API data
• Using Airflow to automate workflows
• Creating dashboards with Power BI
• Deploying data pipelines to Azure or AWS

Upload your projects to GitHub so employers can review your work.

Common Data Engineer Interview Questions

Employers often ask about:

• SQL
• Python
• ETL
• Data Modelling
• Data Warehouses
• Snowflake
• Airflow
• Spark
• Cloud Platforms
• Performance Optimisation

Interviewers may also ask you to explain previous projects and architectural decisions.

Salary Expectations

Data Engineering remains one of the highest-paying careers in technology.

Typical annual gross salaries in Malta include:

Professionals with expertise in cloud platforms, Snowflake and large-scale data systems often command the highest salaries.

Is Data Engineering a Good Career?

Absolutely.

Demand continues to grow as companies invest in analytics, artificial intelligence and cloud technologies.

Data Engineering offers:

• Excellent salaries
• Strong job security
• Remote opportunities
• Continuous learning
• Career progression

It’s an excellent career choice for people who enjoy solving technical problems and working with large amounts of data.

Final Thoughts

Data Engineering has become one of the most exciting and rewarding careers in technology.

By building strong skills in SQL, Python, cloud platforms and modern data tools such as Snowflake, Airflow and Spark, you’ll be well positioned for a successful career.

The best way to learn is by combining structured learning with hands-on projects. Start small, build real solutions and continue expanding your knowledge as the technology evolves.

If you’re ready for your next opportunity, browse the latest Data Engineering jobs on SoftwareVacancy and discover companies hiring across Malta.

Frequently Asked Questions

Is SQL enough to become a Data Engineer?

SQL is essential, but most employers also expect knowledge of Python, cloud platforms and data pipelines.

Which programming language should I learn?

Python is the most widely used programming language for Data Engineering, followed by SQL and occasionally Scala or Java.

Which cloud platform is best?

Azure, AWS and Google Cloud are all excellent choices. Pick one and build practical experience before learning the others.

Is Data Engineering difficult?

There is a learning curve, but with consistent practice and hands-on projects, it’s an achievable career path for many software and IT professionals.

Where can I find Data Engineering jobs in Malta?

SoftwareVacancy regularly publishes Data Engineering opportunities from employers across Malta, including junior, mid-level and senior positions.