Looking for jobs in data? You won’t have to look very far. Data careers have become one of the fastest growth fields over the last 10 years, driven by widespread digital transformation, a mass cloud migration (which was then supercharged by COVID-19) and more sophisticated listening tools. In short, the world is awash with data – about 2.5 quintillion bytes per day – and it needs data professionals to organise and analyse all that information.
According to the World Economic Forum’s The Future of Jobs Report in 2020, the top three growth roles – data analysts and scientists, machine learning specialists and big data specialists – all fall under the general data umbrella. The trend is consistent here, too. In 2022, data scientist ranked in the Top 5 most in-demand and highest paying jobs in Australia.
So what are the most popular data careers? And how much do they pay? Let’s find out.
1. Data analyst
Data analysts gather and organise data sets, and then – this is the important bit – analyse that data to inform the wider business and optimise its processes. This could be anything from user traction on social media, to purchasing trends on an e-commerce site, or how to improve customer retention through loyalty programs. Data analysts are curious by nature. They want to know the why behind the numbers, and then communicate those findings to stakeholders within the business. You can become a data scientist with a bachelor’s degree, or even an online diploma.
Average salary: $95k
2. Data engineer
Data engineer is a slightly more advanced data career, and usually involves a master’s degree, or a bit of experience working as a data analyst, or both. While data analysts deal with data acquisition and processing, data engineers actually build the algorithms and architectures that capture that data in the first place. They need a solid understanding of programming, statistical methods, and SQL. Some machine learning and ETL knowledge wouldn’t go astray, either.
Average salary: $125k
3. Data scientists
Data scientists and data analysts perform similar roles, but there is a fine distinction. While analysists examine and collect data, scientists are more concerned with creating the framework and operational models for that analysis to occur. This usually involves a lot of statistical models and algorithms, running tests, developing products, and optimising data collection frameworks. In-depth programming knowledge of SAS, R and Python will also come in handy.
Average salary: $125k
4. Business analyst
Business analysts use data techniques all the time, but their focus tends to be on business processes and efficiencies. Figuring out how to get the most out of teams, where the gaps are, or creating financial models to support business decisions. They tend to work in Information Technology departments, but they move around the business a lot, talking to stakeholders and leading project teams. You’ll need a good knowledge of basic data skills, SQL, Excel and enterprise architecture.
Average salary: $115k
5. Machine learning engineer
Machine learning engineers are essentially programmers. However their primary focus is building algorithms, models and frameworks to allow ‘machines’ to learn and work independently. They’re basically AI architects. Data scientists might build some models, but machine learning engineers have to transform those models into actual code, and then scale that code for production. This is a massive growth field. You’ll need to be fluent in languages like Java and Python, with sprinkling of computer science, maths and statistics, too.
Average salary: $115k
6. Data architect
Think of data architects as the creators of data structures. Instead of physical buildings, however, data architects map out the frameworks and warehouses needed to acquire, organise, store, analyse and – most important of all – use data. They’ll often work closely with data engineers for this purpose, organising how data flows through an organisation. Where to store it. How to access it. And how to protect it. Most data architects have a post-graduate data degree, and you’ll need a solid knowledge of data mining, Python and Java, machine learning and SQL.
Average salary: $170k
7. Marketing analyst
Everyone works in data now, and that’s especially true in marketing. Marketing managers are expected to have some basic data skills, but the real heavy lifting (outside Google Analytics) is still mostly done by marketing analysts. These are essentially data analysts with a marketing skew. They help companies understand their customers better. How they think, and how they purchase. Marketing analysts conduct a lot of market research, and use data to help inform and design marketing strategies. Communication is a big part of this role, so you’ll need to be able to visualize data patterns, and then translate those patterns for internal stakeholders.
Average salary: $105k
Want to kick-start your data career? RMIT Online has a range of data degrees and short courses available.