Data Engineer
Data engineers build the pipelines and storage that move an organisation's data from the systems that create it to the analysts and machine learning teams who use it.
- Median salary*
- $111,800
3.7%vs last year, before tax
- People employed
- 3,100
3.3%vs last year
- Projected growth*
- +22%
to 2035
- AI exposure*
- Moderate
- automation risk
- Average hours*
- 40/wk
matches all-jobs average
- Shortage status*
- In shortage
national
Data engineers design and maintain the pipelines that carry data from source systems into warehouses, lakes and the tools analysts and data scientists rely on. The work sits upstream of analysis: where an analyst interprets the numbers, an engineer builds and looks after the systems that deliver clean data in the first place. Most work inside a cloud platform such as AWS, Google Cloud or Azure, usually as part of a wider engineering or data team.
How much do data engineers earn?
The median full-time salary for a data engineer is $111,800 per annum, before tax, up $23,000 since 2018.
Pay moves most with the depth of your platform skills and the sector you work in, and financial services and technology employers generally pay above government and not-for-profit employers. Contract and day-rate work is common in this field and can pay well, but it comes without paid leave or a guaranteed next engagement. Seniority, and whether you can design systems as well as build them, matters more than location.
What does a data engineer do day to day?
The list below is what fills most weeks; the exact mix shifts with seniority and whatever stage the current work is at.
- Untangling why a pipeline broke overnight, before anyone downstream notices their dashboard is wrong
- Rewriting a slow, clunky data flow into something that holds up as volumes grow
- Backfilling historical data after a bug is found, without corrupting the reports still reading the old numbers
- Sitting with an analyst or data scientist to work out what their model actually needs from the data
- Keeping an eye on cloud costs and infrastructure health so nothing quietly breaks the budget or the build
What skills do data engineers need?
Employers look for programming and software development, cloud infrastructure, data analysis, backed by Apache Spark fluency and strong problem solving.
Specialist skills
- Programming and software development
- Cloud infrastructure
- Data analysis
- Project management
- Networks and systems administration
- Software testing and QA
Software and tools
- Apache Spark
- SQL
- Python
- AWS / Google Cloud / Azure
- Airflow / dbt
General skills
- Problem solving
- Attention to detail
Is the job growing?
About 3,100 people work as data engineers in Australia, and employment is projected to grow 22% over the decade to 2035. That's very strong growth. Few roles in Australia are expanding this fast, and it points to solid demand for years to come.
How do you become a data engineer?
Here's the path most data engineers take, step by step.
- 1Start with a degree in computer science, software engineering or a data discipline
About 52% of data engineers hold a Bachelor degree, and the subjects that matter most are programming, databases and algorithms. A diploma or advanced diploma in information technology can also get you started, particularly if you build cloud and SQL skills alongside it.
- 2Get properly fluent in SQL, Python and one cloud platform
Employers expect you to query and transform data without hand-holding, and to know your way around a warehouse, storage buckets and scheduled jobs. Personal projects, even small ones that move real data end to end, carry more weight with junior hiring managers than course certificates alone.
- 3Take a first role through a graduate program, an analyst job or a junior engineering position
Data engineers often start as data analysts, business intelligence developers or software engineers and move sideways once they have pipeline and platform experience. Graduate programs at banks, insurers, retailers and government agencies are another common entry point.
- 4Add a platform certification if it helps your applications
Certifications from AWS, Microsoft or Google cover the data services you will use daily and suit people changing careers without a computing degree. They are not a substitute for being able to build and debug a pipeline, so treat them as a way to get past an initial screening.
Study routes we have checked
Qualifications that are a recognised way into this role, and what else you need.
- Bachelor of Business/Bachelor of Data Science
A recognised route into this role
Complete relevant data-science coursework and projects. Data engineering also requires practical database and programming capability; employer requirements vary.
- Bachelor of Computer Science/Master of Data Science
A recognised route into this role
Complete relevant data-science coursework and projects. Data engineering also requires practical database and programming capability; employer requirements vary.
- Bachelor of Data Science
A recognised route into this role
Complete relevant data-science coursework and projects. Data engineering also requires practical database and programming capability; employer requirements vary.
- Bachelor of Mathematics/Master of Data Science
A recognised route into this role
Complete relevant data-science coursework and projects. Data engineering also requires practical database and programming capability; employer requirements vary.
- Bachelor of Science/Bachelor of Data Science
A recognised route into this role
Complete relevant data-science coursework and projects. Data engineering also requires practical database and programming capability; employer requirements vary.
- Bachelor of Science/Master of Data Science
A recognised route into this role
Complete relevant data-science coursework and projects. Data engineering also requires practical database and programming capability; employer requirements vary.
Ready to apply as a data engineer?
Whether you're working toward becoming a data engineer or already are one and want a hand with the next step (sharpening your resume for ATS screening, tightening your cover letter, or knowing what you'll actually be asked at interview), here are examples grounded in this specific role, not generic templates.
What jobs can a data engineer move to?
Moving into Software Engineer typically comes with the biggest pay rise, worth $20,100 a year more on average.
| Move to | Typical pay change | Overlap | Retraining |
|---|---|---|---|
| Software Engineer Engineers can move into software engineering, applying coding and systems skills to broader application development. | +$20,100 | 67% | minimal |
| DevOps Engineer Engineers can move into devops, applying pipeline automation and cloud skills to build and run deployment systems. | +$20,100 | 45% | reskill |
| Machine Learning Engineer Engineers can specialise in machine learning, using pipeline skills to prepare and serve training sets. | +$15,600 | 48% | short course |
| Data Architect Engineers can move into architecture, applying pipeline and modelling knowledge to design organisation-wide structures. | +$13,000 | 27% | reskill |
| Site Reliability Engineer Engineers can move into reliability work, using platform and automation experience to keep large systems dependable. | +$10,400 | 62% | minimal |
Moves are chosen from Jobs and Skills Australia's Data on Occupation Mobility, which follows income tax records between 2011-12 and 2020-21, together with entry requirements and skill overlap. A known move is one people were seen making in that data. Pay change compares median full-time pay for the two roles.
Who works as a data engineer?
The typical data engineer is 36 years old; 79% are men, 92% work full-time, and full-timers average 40 hours a week.
- 36
- Median age
- 21%
- Female share
- 92%
- Full-time
- +0h
- vs all-jobs avg
What's it like being a data engineer?
The week runs between building and fixing. Data engineers spend long stretches designing pipelines and writing code, then drop everything when a load fails or a source system changes shape and the numbers downstream stop making sense. It suits people who prefer systems to meetings and who take satisfaction in something running quietly for months, though the quiet tends to be broken by someone else's deadline.
What people like
- Pipelines that run without anyone noticing. The reward is largely invisible: a warehouse that loads on time every morning and a dashboard nobody has to apologise for.
- Faults usually have a cause. When something breaks there is an answer in the logs or the lineage, and working backwards to it is a puzzle with an end.
- Close enough to see what the data is for. Talking with analysts and data scientists shows how the numbers get used, which makes design decisions easier to argue for.
- The toolset keeps moving. Spark, dbt, Airflow and the cloud platforms change often enough that the work rarely goes stale, and skills learned in one stack mostly carry to the next.
What people find hard
- You are first on the call when something breaks. Pipelines run overnight and on weekends, so failures land at inconvenient times and someone has to sort them out before the business starts its day.
- Upstream of everyone else's deadline. A report, a model launch or a regulatory filing usually depends on your pipeline landing first, which puts other people's timelines onto your work.
- Working with data you did not create. Source systems belong to other teams, and a renamed field or an unannounced schema change can undo a week of work.
- Less visible than the analysis. Dashboards and models get the attention in meetings, while the pipeline feeding them is noticed mainly when it stops.
Based on our synthesis of professional-body surveys and public accounts of the role, not first-person verified reviews.
Which industries employ data engineers?
Professional, Scientific and Technical Services employs the largest share of data engineers, followed by Financial and Insurance Services.
Top employing industries
- 1Professional, Scientific and Technical Services
- 2Financial and Insurance Services
- 3Public Administration and Safety
- 4Information Media and Telecommunications
- 5Electricity, Gas, Water and Waste Services
Ranked by employment share; the source doesn't publish an exact percentage per industry.
| Bachelor degree | 52% | |
|---|---|---|
| Postgraduate | 26% | |
| Diploma / Advanced Diploma | 13% | |
| Other | 9% |
Will AI replace data engineers?
AI reaches this job moderately. Assistants in the editor draft transforms and SQL quickly, and monitoring tools flag failed loads earlier than alerting rules once did, so the mechanics of building and watching pipelines are exposed. What stays with the engineer is design: deciding what a pipeline should produce, how reliable it has to be, and whether the data coming out is correct.
Share of typical working time by exposure level
- Writing and maintaining pipeline codeCode assistants draft Python and SQL transforms in seconds, but someone still has to specify the logic and test it against real source data before it runs in production.35%high
- Monitoring pipelines and diagnosing failuresAnomaly detection catches a failed load or an odd row count, then finding the cause usually means a person reading logs and lineage.30%moderate
- Data modelling and warehouse designTools can suggest a star schema, but the choice of how facts and dimensions should be structured depends on questions the business has not asked yet.20%moderate
- Talking with analysts and other teams about what data they needSitting with an analyst to work out why a number looks wrong, or which fields a model needs, is still a conversation between people.15%low
Common questions about becoming a data engineer
Straight answers to the questions people ask most.
How much do data engineers earn?
The median for data engineers working full time is $111,800 per year before tax. Pay climbs with seniority and with scarce platform skills, and contract and day-rate arrangements often pay above the permanent equivalent.
How do you become a data engineer?
Most people enter through a degree in computer science, software engineering or a related field, then build SQL, Python and cloud skills on the job or through their own projects. Others arrive from data analysis, business intelligence or software engineering, where querying and coding experience transfers directly.
Are data engineers in demand?
Data engineers are currently in shortage nationally, and employment is projected to grow 22% over the decade to 2035. The occupation is small, with about 3,100 people working in it, so openings cluster in larger organisations that run their own data platforms.
Will AI replace data engineers?
AI tools now write a fair share of the everyday pipeline code, so parts of the job are exposed to automation. The design decisions stay with the engineer: what a pipeline should produce, how reliable it needs to be, and whether the data coming out is correct.
What can a data engineer move into?
Machine learning engineering is one direction, using pipeline skills to prepare and serve training sets ($15,600 more). Software engineering draws on the same coding and systems knowledge ($20,100 more), and reliability work uses platform and automation experience to keep large systems dependable ($10,400 more).
Do you need a degree to be a data engineer?
Not always. Employers hiring junior engineers tend to care more about demonstrable SQL, Python and cloud skills than about where you learned them. A diploma plus a portfolio of working pipelines can open the same first role, though some larger employers still screen on a degree.
Related roles
- Data Architect
- Machine Learning Engineer
- Software Engineer
- Site Reliability Engineer
- DevOps Engineer
- Data Analyst
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