2026–27 Australian market
Explore salary benchmarks, market demand, Australian workforce statistics and representation across specialist data and AI roles.
2026 salary guide
Permanent salary ranges and contract day rates, with female and male representation for each role.
| Role title | Permanent | Contract | Female | Male |
|---|---|---|---|---|
| Data Analyst | $120k - $180k | $700 - $1,200 | 41% | 59% |
| Data Engineer | $120k - $200k | $800 - $1,200 | 28% | 72% |
| Data Scientist | $140k - $220k | $800 - $1,250 | 27% | 73% |
| Data Architect | $160k - $240k | $900 - $1,300 | 16% | 84% |
| AI Engineer | $160k - $200k | $950 - $1,200 | 20% | 80% |
| AI Platform Lead | $150k - $240k | $1,000 - $1,200 | 26% | 74% |
| AI Solution Architect | $170k - $250k | $1,100 - $1,300 | 15% | 85% |
| Machine Learning Engineer | $140k - $190k | $900 - $1,100 | 22% | 78% |
| MLOps Engineer | $120k - $185k | $850 - $1,050 | 20% | 80% |
| Governance & Ethics Lead | $130k - $200k | $900 - $1,300 | 40% | 60% |
| Customer Insights Analyst | $120k - $180k | $700 - $1,200 | 48% | 52% |
| BI Analyst/Developer | $80k - $140k | $600 - $900 | 35% | 65% |
| AI Manager | $150k - $210k | $1,000 - $1,250 | 40% | 60% |
| AI Strategy Lead | $160k - $270k+ | $1,100 - $1,500 | 45% | 55% |
| Data Engineering Manager | $180k - $250k | $1,100 - $1,500 | 38% | 62% |
| Head of MLOps/ML | $200k - $300k+ | $1,200 - $1,600 | 15% | 85% |
| Head of Analytics | $200k - $300k+ | $1,200 - $1,600 | 28% | 72% |
Market overview
The Australian data market continues to reward strong foundations, while the premium shifts towards commercial impact.
The Australian data market is moving from reporting towards decision-making. Businesses still need strong foundations in data engineering, analytics and BI, but the premium is increasingly on people who can turn those foundations into commercial outcomes.
AI is accelerating that shift rather than replacing the need for data capability. Jobs and Skills Australia (JSA) found that 31% of Australia's workforce is in occupations highly exposed to augmentation by generative AI, compared with just 4% in occupations highly exposed to automation. That distinction matters: the opportunity is increasingly about people working effectively with AI, not simply doing the same work faster.
At the specialist end of the market, Data Scientists remain a relatively small talent pool. JSA's latest occupation data puts the Australian workforce at approximately 3,400 Data Scientists, with 72.7% male and 27.3% female.
Where demand is growing
Demand spans emerging AI disciplines and the data foundations required to deploy them responsibly and at scale.
Market insight
The strongest candidates increasingly sit between technology and the business. Technical depth still matters, but employers are placing greater value on people who can explain what the data means, influence decisions and connect analytics investment to revenue, productivity or risk.
Australian market stats
Jobs and Skills Australia data highlights both AI's augmentation potential and the constrained specialist talent pool.
of Australia's workforce is in occupations highly exposed to Gen AI augmentation, compared with 4% highly exposed to automation.
Jobs and Skills Report 2025Data Scientists are currently employed in Australia, highlighting the relatively small pool available for specialist data and AI hiring.
Jobs and Skills Australiaof Australian Data Scientists are women, highlighting the continued diversity gap within specialist data roles.
Jobs and Skills AustraliaDiversity snapshot
The gender gap widens significantly in the more technical and emerging areas of the market.
Across the Data, Analytics, AI & BI roles analysed by Tech & Data People, women represent 29.6% of the talent pool, with the gender gap widening significantly in the more technical and emerging areas of the market.
The data shows a clear divide between commercially focused analytics roles and specialist technical disciplines:
Technical and AI roles remain heavily male-dominated. Women represent just 15–22% of AI Solution Architects, Data Architects, MLOps Engineers and Machine Learning Engineers.
Data Architecture has one of the widest gaps, with women accounting for just 16% of the talent pool.
AI leadership is somewhat more balanced. Women represent 40% of AI Managers and 45% of AI Strategy Leads, suggesting stronger female representation as roles move towards strategy, governance and business leadership.
Analytics and insights roles are closer to gender parity. Customer Insights Analysts are 48% female, while Data Analysts sit at 41% female.
Data Engineering remains strongly male-dominated, with women representing 28% of Data Engineers and 38% of Data Engineering Managers.
Governance & Ethics is one of the more balanced specialist areas, with women representing 40% of the roles analysed.
The gap becomes particularly pronounced at the top of technical disciplines. Women account for just 15% of Heads of MLOps/ML, compared with 28% of Heads of Analytics.
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