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Do you know what you want from your next Data Engineer job? The opportunity to make an impact in a forward-thinking start-up or shake up an ASX-listed organisation? We can support you every step of the way to realise your potential. 

Our network of Australia’s top employers means we have roles you can get excited about and the expertise to support you to secure them. 

Find your nearest office to get in touch with us, send us your CV or browse our latest available Data Engineer jobs.  

Latest Data Engineer jobs

Senior Software Engineer - QDAT
NSW - Sydney CBD
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AWS Data Engineer
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Senior Insights Analyst
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Your Data Engineer job questions, answered

Where can I find Data Engineer jobs in Australia?

Hays regularly advertises new jobs for Data Engineers right around Australia. Click below to check out all our Data Engineers jobs or those in your nearest city: 
All Data Engineer jobs Australia
Data Engineer jobs Adelaide 
Data Engineer jobs Brisbane 
Data Engineer jobs Canberra 
Data Engineer jobs Melbourne 
Data Engineer jobs Perth 
Data Engineer jobs Sydney 


What does a Data Engineer do? 

A Data Engineer works across the architecture, design, development and deployment of data warehouse and business intelligence systems. They develop, build, maintain and monitor databases and processing systems.
Part of this includes building pipelines that extract data, transform it and load it into the data warehouse system so that it can be used by other IT professionals for analysis. 

What skills does a Data Engineer need to have? 

The main skills that a Data Engineer is required to have is designing, building and implementing data engineering and ETL solutions, including building data pipelines and testing overall solutions whilst working with key technical and non-technical stakeholders to achieve business outcomes.
There is an increasing demand and focus in the market for cloud-based solutions across platforms such as AWS and Azure, therefore experience developing solutions in this area is highly sought.  
The Data Engineer should have comprehensive knowledge of SQL, Python, R, and ETL (extract, transform, load) methodologies and practices. These are to be used to ensure that the data pipeline is working. The data pipeline is a sum of tools and processes for performing data integration and the Data Engineer is tasked with managing all aspects of this infrastructure.  
When doing so effectively, a Data Engineer can extract data and process it by building and setting up database systems. These systems can then be used by other stakeholders such as Data Analysts, Business Intelligence Analysts and other Data Engineers.
This aspect highlights the need for a Data Engineer to have good interpersonal skills, as working effectively with other teams will allow the Data Engineer to get a better understanding of what is required to achieve organisational goals.
A Data Engineer should be able to identify the most appropriate manner to complete the process of data warehousing. Those with in-depth knowledge of cloud-based data warehouses and other integration tools will be recognised as having the requisite experience to serve as a Data Engineer. 

What is a Data Engineer’s job description and job responsibilities? 

  • Design, develop and maintain data architecture 

  • Assemble and acquire data which meets organisation requirements 

  • Develop and design processes for data optimisation 

  • Build the frameworks required for large data sets 

  • Use programming languages and data analysis tools that is reflective of organisation goals and metrics 

  • Work with other stakeholders including Data Scientists, who design machine learning and AI models, and deploy these into data pipeline 

  • Deliver updates and analytics to stakeholders 

What skills and experience are employers looking for from Data Engineers? 

With respect to Data Engineers, employers have made it clear that they are placing more emphasis on experience rather than education.

This means that there are great opportunities for those with the requisite skills outlined below: 


Core Skills  

  • Communication and Teamwork 

  • Presentation  


Technical Skills 

  • Programming Languages 

  • Database Systems  

  • Data Warehousing   

  • Big Data 

Core Skills 

Strong communication skills are a key component of a Data Engineer’s role. On any given day, they may have to liaise with a list of IT professionals including Data Scientists, Machine Learning Engineers, Data Analysts, and a range of developers. In addition, they may work with other teams to acquire the necessary information required to define the scope of a project. Working with other key stakeholders emphasises the importance of effective communication during what is often a collaborative task. 

Employers have made it clear that being able to excel in a team environment is important. A Data Engineer is required to work with other IT professionals, all of whom depend on one another for deliverables. Consequently, there is a need for a healthy working relationship and the ability to collaborate effectively for projects to run smoothly. Those who have displayed strength in this area through the delivery of previous projects will be in a favourable position.   

The requirement for sound presentation skills is rising on the list of skills that employers are looking for. One of the things we have noticed here is that many key stakeholders do not possess the technical knowledge needed to understand the large-scale data analysis that is conducted by Data Engineers. As a result, it is becoming increasingly important for Data Engineers to present their findings in a manner which is easy for stakeholders outside of IT to understand.    

Technical Skills 

As is the case with many IT roles, Data Engineers need to have a sound understanding of programming languages. These languages are required to code the data infrastructures that support business information systems and applications. Employers have listed Python as the most common language for data analysis and scripting, but we recommend Data Engineers understand multiple languages.  

Similarly, employers expect Data Engineers to be proficient in SQL and NoSQL database systems. This knowledge is considered essential as they are tasked with creating the data systems that will house the data along with the way it will be organised and found.  

A complementary skill that employers seek experience in is data warehousing. Data Engineers should be able to use tools to store, analyse and process data. Hadoop, Hive and Kafka are often referenced as desirable knowledge by employers as they allow for building robust and integrated data infrastructure. 

What type of employers hire Data Engineers? 

Data is becoming the modern fuel for organisations. It is being used more than ever to solve business problems and introduce new technologies that can drive success.

Some of the major industries employing the services of a Data Engineer are:  
  • Information Technology – A surge in data and the creation of new systems such as cloud computing has paved the way for a substantial number of Data Engineer jobs. With more data to be stored and analysed, the value that a Data Engineer can bring to these organisations should not be underestimated. 
  • Financial Services – A high volume of Data Engineers are sought in financial services as they have large amounts of data that needs to be store in very robust data warehouses with good data pipelines and established data governance. In addition, organisations within financial services are also starting to invest in big data platforms more, requiring Data Engineers. 
  • Healthcare – The role of technology in healthcare is growing at a rapid rate. From the systems that now store data to the way it is managed and analysed, there is high demand for Data Engineers. 
  • Telecommunications –  Due to the large volume of data collected within this industry, from the growing use and range of data driven applications available in the market now, there is a continued demand for good data engineers in this area. This is to ensure that the organisation has hardy enough data systems to handle this increased volume of data and to ensure that the data is stored and flows through each system correctly. 
Other fields include finance, engineering, manufacturing, energy, and public administration. 

What technologies does a Data Engineer use? 

  • Programming Languages: Python, Java, Scala, R and Ruby among others  

  • Database Management Software: SQL and NO SQL  

  • Cloud Migration: AWS, Microsoft Cloud Azure, Google Cloud Platform  

  • Data Warehousing: PostgreSQL, Hadoop, Hive, Kafka and others  

How much do Data Engineers earn in Australia? 

With IT becoming a pivotal part of a growing number of organisations, Data Engineers are being duly rewarded for their skills. The value placed on data is creating more Data Engineering jobs including specific roles such as Big Data Engineer and Azure Data Engineer.
Salaries for Data Engineers can vary widely depending on the specific role, location and the type of company they work for. Data Engineer salaries tend to range between $110,000 and $180,000 in major cities like Sydney, Melbourne and Canberra. 
For our latest guide on typical salaries as a Data Engineer, please refer to our Hays Salary Guide.

How can I become a Data Engineer in Australia? 

As a relatively new field there are no formal Data Engineer qualifications. We have found that the most common educational backgrounds include bachelor’s degree in Computer Science, other IT related disciplines and Mathematics.

Currently, employers are more focused on experience rather than education. This means that it is essential to develop the technical skills required to be a Data Engineer. This should start with mastering SQL, Python and R along with ETL methodologies.

Expand your knowledge. This can be with respect to programming languages or data technologies and tools (Hadoop, Spark, Hive etc). Similarly, cloud computing services are consistently being noted by employers including AWS and Microsoft Azure.

Gain experience in software in important areas such as software development and data science with emphasis on getting credited where possible. Being able to show certification for skills on your resume will help you stand out.

Work on your core skills as they could be the differentiator. Employers are placing significant value on the ability to communicate effectively and collaborate within a team environment.