Data Engineer jobs - Main region

Data Engineer jobs


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 by 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.

Data Engineer 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

Skills and experience employers are looking for 

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 – An increase in the amount of data collected has led to Big Data Engineer roles in this industry. Voice and SMS used to be the only mediums on this platform but since the introduction of the smart phone, the amount of data has risen exponentially. Social media activities, internet browsing and the growing use of applications has increased the value of data in this industry and the subsequent need for Data Engineers.
Other fields include finance, engineering, manufacturing, energy, and public administration.

Technologies that a Data Engineer uses 

  • 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 
  • Communication Platforms: Email, Slack, Teams, Zoom, Google Meets etc 
  • General Software: MS Office or equivalent 

How much do Data Engineers earn?

 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 Engineer jobs including specific roles such as Big Data Engineer and Azure Data Engineer. For our latest guide on typical earnings as a Data Engineer, please refer to our Hays Salary Guide.

How to become a Data Engineer in Australia

  1. 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.  
  2. 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. 
  3. 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. 
  4. 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. 
  5. 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.  

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