By 2026, the race to hire top AI and data talent has never been fiercer. In 2025, hiring of AI engineering talent grew by over 25% year-over-year - Data from the Economic Graph shows AI‑engineering roles now make up nearly 7% of all technical job postings.
Why AI Engineers Aren’t Enough
AI engineers are in high demand: they build machine learning models, deploy generative AI, and deliver analytics that can transform a business. But without strong data infrastructure, their work often stalls.
Data engineers - the forces building pipelines, data lakes, and ETL systems - remain vital. They provide the clean, structured data that powers all AI initiatives.
Companies that ignored data engineering in their AI hiring strategy in 2026 will face problems.
The Numbers Tell the Story
- AI Role explosion (2025): According to Autodesk’s AI Jobs Report, listing for “AI Engineer” themselves are up +143.2% YoY which is huge! .
- AI Fluency Becoming a Baseline: Autodesk’s report also shows that mentions of “AI” as a required skill in jobs have surged +56.1% in 2025, on top of earlier jumps.
- This data is interesting because businesses need to make sure they don't neglect data engineers - they must have in their AI strategy 2026.
Evidence: A study by Databricks found that only 22% of enterprises believe their IT infrastructure is ready for AI.
- Nearly 8 out of 10 companies are trying to deploy AI on infrastructure that isn’t prepared - meaning pipelines, data lakes, and ETL processes aren’t ready.
- Why it matters: AI engineers need clean, reliable, and scalable data to train models. Without data engineers to build and maintain these pipelines, AI teams can’t function effectively.
Case Study: How Cerby Sequenced AI Hiring for Maximum Impact
When Cerby began planning their AI capability, they realised a common problem: their data was distributed across multiple sources, each with different levels of structure and cleanliness.
Working with Hubscale, Cerby intentionally hired their Data engineer three months before their AI engineer to give the AI specialist the space to:
- audit existing data sets
- define training requirements
- establish what “good” data needed to look like
- shape the role specification for the incoming data engineering hire
By the time their data engineer joined, the roadmap was clear and actionable - enabling faster pipeline creation and a smoother model development process.
What to Expect in 2026
- Hybrid Roles Are Emerging: Positions like ML Data Engineer or AI Infrastructure Architect bridge the gap between pure data and pure AI.
- Cross‑Functional Upskilling: Data engineers will increasingly pick up AI skills (ML, prompt engineering), while AI engineers are learning more data engineering.
- Ethics & Compliance Matter: Hiring isn’t just about model skills - companies are increasingly asking for data‑ethics, AI governance, and security experience.
- Balanced Teams Win: Organisations that invest in both data and AI talent are seeing faster, more reliable outcomes.
Actionable Takeaways for Businesses
- Build Data Foundations First: Make sure your data team is strong - AI engineers can’t do much without reliable pipelines.
- Hire Hybrids: Look for engineers with both data and AI skills - they’ll be especially valuable in 2026.
- Upskill Strategically: Encourage your AI and data teams to cross-learn.
- Track Market Trends: Use labor market data (LinkedIn, Autodesk reports) to inform your hiring roadmap and stay ahead.
Looking Ahead
2026 will reward companies that don’t just chase AI talent - but also build the infrastructure to power it. The future belongs to teams where AI engineers and data engineers collaborate deeply. If you want your AI strategy to succeed, don’t just hire for AI — hire for data too.
If you’re building your first AI, data or GTM function in cybersecurity, get in touch - we’re currently advising early-stage companies on 2026 talent planning