Data Engineer vs Data Scientist: Salary, Skills, and Growth in Australia
Australia’s job market is evolving due to the rising adoption of AI, and the comparison between data engineering vs data science is increasing. This evolution resulted in an increasing demand for Data scientists across various sectors, particularly finance, healthcare, and technology. Companies are relying more on data scientists to handle large quantities of data assets to drive business decisions. Sources predict a 25% yearly growth in data science skills worldwide. Starting salaries range from A$85,000 to A$100,000 and reach A$170,000 in advanced roles.
On the other hand, Data engineers are still essential but have a slightly lower growth rate compared to data scientists in Australia. They are offered A$70,000–A$90,000 in entry-level roles. Lead data engineers often command up to A$190,000. Data engineering is also ranked high among top-paying jobs in Australia. Their role is to build infrastructures that organisations use to manage data.
Both roles offer excellent career growth and an opportunity to work in a progressive environment. Data scientists can move into management roles like Chief Data Scientist, while Data engineers can advance into senior positions like Data Architect.
How Data Engineers and Data Scientists Work Together
Think of building a house. The data engineer is the architect and the construction team that constructs the foundation and ensures the structure is solid. While the data scientist is the interior designer who comes in after the construction to make the house beautiful and functional, properly optimised for living.
The Data Workflow:
Data engineers focus on building and maintaining data pipelines, managing large databases, and ensuring data flows effectively across the system.
Data scientists, on the other hand, work on analyzing the data that data engineers have prepared and use statistical understanding, machine learning algorithms, and data visualisation tools to extract valuable data so that businesses make informed decisions.
I remember when my friend became a data engineer; he rarely had time on weekends. He had a lot to learn, from data processing tools to programming languages and cloud data warehouses. But according to him, a career in the data science field is relatively easier to pursue. For that, you need to learn two fundamentals: a programming language like Python and a data query language such as SQL. But advanced stages require a PhD. We’ll further break down both the roles, their responsibilities, and expected salaries below. But if you need someone to guide you personally through each step of your journey, we’re just a call away.
Data Engineer vs Data Scientist: Salaries in Australia 2025
Let’s explore the primary factor that most professionals want to know first: the salary. Both roles offer attractive salaries, but there are significant differences based on location and experience.
Data Engineer Salary Breakdown
Experience Level | Years of experience | Average Annual Salary (AUD) |
Entry-Level
| 0-2 years | $70,000 - $90,000 |
Mid-Level | 3-5 years | $115,000 - $125,000 |
Senior-Level | 8+ years | $155,000 - $190,000 |
Based on recent market analysis, the median salary for a data engineer in Australia is approximately $135,000 AUD in 2025. In major cities like Sydney, it can reach up to $155,000 annually, with lead data engineers making up to $200,000 AUD per year.
Data Scientist Salary Breakdown
Experience Level | Years of experience | Average Annual Salary (AUD) |
Entry-Level | 0-2 years | $85,000 - $100,000 |
Mid-Level | 3-5 years | $115,000 - $135,000 |
Senior-Level | 8+ years | $137,000 - $170,000 |
Based on a recent survey, the average salary of data scientists in Australia is approximately $150,000 per year. Senior data scientists in a leading company like Commonwealth Bank of Australia can earn compensation exceeding $170,000. Approximately 5000+ jobs are listed on LinkedIn Australia.
Both fields show strong salary advancement, with data engineering expecting faster growth over the coming years, as the demand for professionals who can build scalable data infrastructure is increasing.
Data Engineering vs Data Science: Which Field is Growing Faster?
While both domains are growing at an exceptionally fast pace, the data science job market is expanding globally. The U.S. Bureau of Labour projects a 34% growth from 2024 to 2034.
Data engineering and big data roles show a slightly low growth rate than data science in the global market; the percentage is around 13.33% (CAGR 2024-2030)
The demand for data scientists in Australia is expected to increase 25% over the next 5 years, making it a great choice. The data engineering market is not very welcoming to fresh graduates, but it may differ for senior roles.
Here's what makes both fields attractive:
Data Engineers
Cloud computing adoption is increasing; IDC reports, Australian cloud spending to increase to A$22.4 billion in 2026
Real-time data processing is becoming essential
Shortage of experienced professionals with infrastructure skills
The data ecosystem is becoming complex
Data Scientists
Companies are relying more on data-driven business decisions
ML and automation are integrated across industries
Data science roles are expanding across multiple sectors like healthcare, retail and finance
Growing need for business intelligence and predictive analysis
Contract Worker Salary: Data Engineer vs Data Scientist
According to the Q1 Data Market Report 2025, contract jobs for both roles are gaining momentum. Long-term and 6-9-month contracts are normal in these industries. As companies are focusing on cutting expenses, contractors should adjust accordingly.
Position | Salary per day |
Data Engineer | $900-$1200 |
Senior Data Engineer | $1200-$1400+ |
Data Scientist | $900-$1100 |
Senior Data Scientist | $1100-$1400 |
Roles & Responsibilities of Data Engineer and Data Scientist
Data Engineer | Data Scientist |
Design and develop scalable data pipelines using ETL processes | Analyze complex datasets to identify trends and patterns |
Build and manage data lakes & data pipelines on cloud lakes | Using ML algorithms to build predictive models |
Ensure data quality and consistency while maintaining security across systems | Create dashboards and data visualisations for stakeholders |
Database performance and query optimization | Test hypotheses by conducting statistical analysis also experimenting different analytical approaches |
Collaborate with data scientists to deliver clean, accessible data | Develop forecasting systems and recommendation models |
Implement automation for data collection and processing | Effectively communicate insights to non-tech stakeholders |
Utilise Technologies like Apache Spark, Airflow and Kafka | Collaborate with product team to solve problems |
Without data engineers, data scientists would spend most of their time cleaning and sorting data rather than analysing it. A data scientist’s job is to extract meaning from data infrastructures that engineers built to help shape the company’s strategy.
Data Engineer Skill Development Path
Foundation Level (0-6 months)
Target Role: Junior Data Engineer/Data Engineering Intern
Expected Salary in Australia: $70,000 to $90,000 AUD
Python programming fundamentals
SQL and relational database concepts
Linux/Unix systems (Basic understanding)
Version control with Git
Understanding of data structures and algorithms
Intermediate Level (6-18 months):
Target role: Data Engineer/Mid-Level Data Engineer
Expected Salary in Australia: $95,000 to $125,000 AUD
Advanced SQL and query optimization
ETL/ELT pipeline development
Apache Spark for big data processing
Cloud platforms (AWS, Azure, or Google Cloud)
Data warehousing concepts (Snowflake, Redshift)
Apache Airflow for workflow orchestration
NoSQL databases (MongoDB, Cassandra)
Docker and containerization basics
Advanced Level (18+ months)
Target role: Senior Data Engineer/Lead Engineer/Principal DE
Expected Salary in Australia: $135,000 to $190,000 AUD
Real-time data streaming (Kafka, Kinesis)
Data lake architecture and implementation
Infrastructure as Code (Terraform)
Kubernetes for orchestration
Data governance and security
Performance tuning and optimization
Distributed computing systems
Building scalable microservices
Data Scientist Skill Development Path
Foundation Level (0-6 months):
Target role: Data Analyst/Junior Data Scientist/Analytics Intern
Expected Salary in Australia: $65,000 - $85,000+ AUD
Python or R programming
SQL for data querying
Statistics and probability fundamentals
Data cleaning and preprocessing
Basic data visualization (Matplotlib, Seaborn)
Pandas and NumPy libraries
Intermediate Level (6-18 months):
Target role: Data Scientist/Machine Learning Engineer
Expected salary in Australia: $90,000 to $12,000 AUD
Machine learning algorithms and concepts
Supervised and unsupervised learning
Feature engineering techniques
Model evaluation and validation
Data visualization tools (Tableau, Power BI)
A/B testing and experimental design
TensorFlow or PyTorch basics
Natural Language Processing fundamentals
Time series analysis
Advanced Level (18+ months):
Target role: Senior Data Scientist/Lead Data Scientist/Principal Data Scientist
Expected Salary in Australia: $130,000 to $200,000+ AUD
Deep learning and neural networks
Advanced machine learning techniques
MLOps and model deployment
Big data tools (PySpark)
Causal inference methods
Bayesian statistics
Computer vision applications
Reinforcement learning
Domain-specific expertise (finance, healthcare, etc.)
While these roadmaps provide a clear direction, learning alone can feel overwhelming. Having someone who walked the same path and has a better understanding of Australian job culture is vital for your career breakthrough.
Our mentors can help you choose which skill to focus on based on the current market trends, as well as give personalised guidance on your unique journey. They help you avoid common pitfalls, review your projects, and assist you with interview preparation. They provide constructive feedback in a 1:1 session that you don’t find elsewhere. The time and work you save by getting mentored makes it a great investment in your career.
Sandy Y. was stuck in her career. She connected with our mentor that helped her by creating a clear roadmap for her business analyst role, setting deadlines, and motivating her through her personal stories.
Sandy stated, “I feel like she’s taking a business analyst approach with me, so it’s as if I’ve already started learning the role.”
Education Pathways & Certifications
Formal Education for Data Engineers
A bachelor’s degree in computer science, software engineering, information technology, or mathematics is common among data engineers. While a master’s degree can enhance productivity, it’s not mandatory for entry-level jobs.
Alternative Pathways:
Many data engineers transition successfully from software engineering roles. Bootcamps, online courses, and personalised mentorship can offer practical skills for career changers.
Formal Education for Data Scientists
Data scientists mostly hold a bachelor's degree in data science, statistics, mathematics, computer science, or related quantitative fields. Advanced degrees such as master's or PhD are common among senior DS roles, but now, they are becoming less compulsory with the right skillset and portfolio.
Recommended Certifications
Subdomains and Specializations
Data roles are not just one career path anymore; they are evolving into a rich ecosystem of specialised jobs. And if you are considering a career in data, you’re on the right track. Let’s explore the opportunities and challenges in these roles. Learn what’s trending and where you might fit in.
Data Engineering Specializations
It’s interesting how employers value individuals in the data engineering field who are well-rounded professionals, seemingly switching between building data pipelines, managing cloud infrastructure, and creating visualisations.
Cloud Data Engineers work with Azure, AWS, and Google Cloud to build scalable solutions. The salary range is between $120,000 and $140,000.
Big Data Engineers handle large datasets with Hadoop, Spark, and distributed systems. Coursera reports that entry-level data engineers earn about $128,000 in Australia. Meta pays $202,000+ to its senior data engineers.
Machine Learning Engineers serve as a bridge between data engineering and data science. They deploy production-ready AI systems and earn around $150,000 in Australia. Senior-level ML engineers earn approximately $175,000.
Data Security Engineers protect data breaches and save sensitive information by implementing compliance frameworks and security protocols.
Data Science Specializations
Data science is a vast field with diverse roles offering competitive salaries. Roles involve transforming raw data into actionable figures for businesses.
Machine Learning Scientists often require advanced-level degrees, such as a master's or PhD in statistical and quantitative fields. Their work involves ML algorithm innovation and theoretical research. They are rewarded salaries around $172,000+ in Australia.
Business Intelligence Analysts translate data into business strategies for decision-makers within organisations to understand complex information. They often earn around $125,000 AUD.
Data Visualization Specialists transform data into appealing visuals such as charts and graphs for diverse audiences to understand quickly. Their earnings at the senior level may reach $140,000 in Australia.
AI Research Scientist often work in universities and R&D labs. An advanced degree in AI and computer science with a published research portfolio is a must for this role. Experience in simulation environments and high-performance computing is essential. Artificial research scientists with 8+ years of experience earn approximately $178,000 in Australia.
Career Specializations Table - Australian Market 2025
Specialization | Field | Key Focus | Core Technologies | Salary Range (AUD) | Experience Level |
Cloud Data Engineer | Data Engineering | Building cloud-based data solutions | AWS, Azure, Google Cloud, Snowflake | $120,000 - $140,000 | Mid-level |
Big Data Engineer | Data Engineering | Processing massive-scale datasets | Hadoop, Spark, Kafka, distributed systems | $128,000 | Mid-level |
Machine Learning Engineer | Data Engineering | Deploying production ML systems | Python, TensorFlow, PyTorch, MLOps | $150,000 - $175,000 | Entry to Mid |
Data Security Engineer | Data Engineering | Protecting data with security frameworks | Encryption, IAM, compliance protocols | $130,000 - $150,000 | Mid to Senior |
Machine Learning Scientist | Data Science | Developing complex ML algorithms | Advanced ML frameworks, Python, R | $172,000 | Mid to Senior |
Business Intelligence Analyst | Data Science | Creating actionable business dashboards | Tableau, Power BI, SQL, Excel | $85,000 - $125,000 | Entry to Mid |
Data Visualization Specialist | Data Science | Designing compelling data visuals | D3.js, Tableau, Python visualization | $140,000 | Mid-level |
AI Research Scientist | Data Science | Conducting cutting-edge AI research | Deep Learning, NLP, Computer Vision | $178,000+ | Senior (PhD) |
Data Scientist | Data Science | Transforming data into business insights | Python, SQL, Machine Learning, Statistics | $115,000 - $135,000 | Mid-level |
Senior Data Scientist | Data Science | Leading data science initiatives | Advanced analytics, team leadership | $140,000-$170,000 | Senior (5+ years) |
In-demand Technologies for Data Professionals
Programming languages: Python, Java, SQL | Machine Learning: Scikit-learn, Tensorflow, PyTorch, XGBoost |
Data Processing: Apache Spark, Apache Kafka, Apache Airflow, dbt | Visualization: Tableau, Power BI, Matplotlib, Seaborn, D3.js |
Cloud Platforms: AWS, Microsoft Azure, Google Cloud Platform | Data Analysis: Pandas, Jupyter Notebooks |
Database: PostgreSQL, MySQL, MongoDB, Cassandra (NoSQL), Snowflake, Redshift |
|
Tools You Will Use as a Data Engineer:
Tools You Will Use as a Data Scientist:
Getting Started: Your Next Steps
While both roles offer strong career growth and high demand in Australia, the reality is neither choice is “better”; they are simply different paths that suit different personalities and interests.
If you prefer working behind the scenes on building infrastructure and datasets, want greater compensation in a short-term period, and come from a software or cloud background, data engineering is for you.
And if you prefer analysing data, applying ML for business, and presenting your findings to stakeholders, data science is for you.
The key is to start learning skills and tools for your chosen path.
Build hands-on projects and a strong GitHub portfolio, whether it’s through predictive models or cloud pipelines.
Network with data professionals in your industry.
Participate in Kaggle competitions.
Learn from online courses and bootcamps.
Consider mentorship for personalised guidance.
Ready to Find Your Fit in Data?
Whether you’re starting out in the dynamic data sector in Australia or considering transitioning to a more rewarding career, our experienced mentors are here to guide your path.
Take time to explore both roles, try learning foundational skills, and see which resonates with you. Your data career awaits.
Frequently Asked Questions
Is data engineering harder than data science?
Neither is essentially harder; they require different skills. Strong programming skills and system design knowledge are required for data engineering, while advanced mathematics and statistical knowledge are mandatory for data science. Your background will determine which feels more natural.
DE may feel much harder due to higher standards expected for QA. As a data scientist, it is much easier to impress executives with fancy charts and new insights.
Can I transition between data science and data engineering?
Yes, you can transition between both roles. Data engineers can transition to data science by developing machine learning skills, while data scientists can transition to data engineering by strengthening their software development skills. Overall, the job market for DE is better, as you don’t need a PhD.
Do I need a university degree?
Most data professionals have a degree, but it’s not absolutely mandatory. If you have skills, relevant certifications, and a strong portfolio to impress recruiters, your chances of getting a job are higher, especially for data engineering roles. Data science roles prefer advanced-level education but may value practical experience.
Which role has a better work-life balance?
Many data engineering professionals state that it has a better work-life balance as compared to data science, but it mainly depends on the organisation and the kind of work. Unfortunately, there are a lot of misconceptions around the data science role requirements, often leading to stress and burnout. But companies have begun to value their competent data scientists and reward them reasonably. DE may need to respond to infrastructure issues outside of regular hours.
How long does it take to become job-ready?
With focused study, personalised mentorship and dedication, you can be job-ready in 6-12 months for entry-level positions. Reaching mid-level knowledge requires 2-3 years of practical experience in either field.
Who gets paid more, a data scientist or a data engineer?
Both roles offer high salaries and job security with the opportunity to grow. Data engineers get salaries around $150K at entry-level as compared to the $135K starting salary of a DS.
Data scientists often earn slightly more at senior level given their expertise in machine learning and statistics. But salary may vary due to factors like location, company size and experience.
