The Data Engineer role today
This guide draws on 1,644 Data Engineer postings from 931 companies, published from March to September 2026.
In 4% of Data Engineer postings, building or applying AI is the job itself.
MLOps Fundamentals appears in 13% of Data Engineer postings.
Automation exposure averages 46 out of 100 across these postings, which is moderate: parts of the routine work can be automated, which makes AI skills more valuable in the role.
Senior positions make up 47% of postings, mid-level 22%. The most common way of working is hybrid, in 63% of postings.
Common skill gaps
The AI skills our analysis of Data Engineer job descriptions most often flags as a gap, with the share of postings where each one comes up.
- Vector Database Management35%
- MLOps Fundamentals29%
- Feature Store Management29%
- LLM Data Pipelines24%
- AI Model Deployment24%
- AI Data Governance20%
Data Engineer salary
Pay comes only from postings that quote it: 159 of the 1,644 Data Engineer postings. Each country uses the pay period its employers quote most.
Netherlands
based on 132 postings quoting pay per month€5,500
median per month
The middle half of advertised salaries, with the line at the median.
Essential Data Engineer skills
The skills employers ask for in Data Engineer job descriptions, from the most requested down.
Core skills
- SQL
- Python
Often requested
- Cloud Data Platforms
- Data Modeling
- Data Pipeline Automation
Also valued
- Data Pipeline Orchestration
- Data Governance
- MLOps Fundamentals
- Pyspark
- Data Quality Monitoring
AI skills to learn next
The AI skills employers most often want to add to this role, beyond the ones above.
- MLOps
- Vector Databases
- Feature Engineering
- Feature Store
- Data Versioning
How the Data Engineer role is evolving
The directions employers are taking this role as they adopt AI, with the skills and responsibilities each one adds.
Most common direction
Toward data & machine learning
Typical focus
AI & analytics and AI & ML pipelines
New responsibilities
- Design and maintain data pipelines for ML model training and inference
- Collaborate with data scientists to productionize ML models
- Implement feature stores and vector databases to support AI applications
- Design and maintain data pipelines for machine learning models and AI applications
Other directions
Toward AI transformation
Typical focus
AI enablement
Skills to add
- Change Management
- AI Strategy
- AI Governance
- Data Governance for AI
- AI Platform Architecture
- AI Tool Evaluation
New responsibilities
- Develop and enforce data governance policies for AI and analytics use cases
- Mentor team members on AI-augmented data engineering practices
- Lead the adoption of AI-ready data architectures across the organization
- Define and execute a roadmap for AI-ready data infrastructure
Toward AI engineering
Typical focus
AI infrastructure, AI platform and AI systems
Skills to add
- Prompt Engineering
- LLM Ops
- Model Serving
- AI Assisted Coding
- AI Infrastructure
New responsibilities
- Build and optimize data pipelines for large language model training and inference
- Integrate vector databases and embedding stores into the data platform
- Ensure data quality and governance for AI training datasets
- Ensure data security and compliance for AI workloads
Toward AI governance
Typical focus
AI governance & quality and AI governance & compliance
Skills to add
- AI Governance Frameworks
- Data Privacy for AI
- AI Compliance
- AI Ethics
- Model Risk Management
New responsibilities
- Ensure compliance with AI regulations (e.g., EU AI Act) in data pipelines
- Implement data lineage and audit trails for AI models
- Ensure compliance with AI regulations and ethical standards in data handling
- Collaborate with legal and compliance teams on AI data usage
Data Engineer FAQ
Will AI replace Data Engineer jobs?
Automation exposure averages 46 out of 100 across these postings, which is moderate: parts of the routine work can be automated, which makes AI skills more valuable in the role. Employers are mostly reshaping the role toward data & machine learning, adding skills such as MLOps and Vector Databases.
What skills do Data Engineer roles require?
The skills employers ask for most are SQL, Python, Cloud Data Platforms, Data Modeling and Data Pipeline Automation.
Which AI skills should Data Engineer candidates learn next?
MLOps, Vector Databases and Feature Engineering are the AI skills employers most often want to add. The most common skill gaps are Vector Database Management and MLOps Fundamentals.
How is the Data Engineer role changing?
The most common direction is data & machine learning. Other directions include AI transformation, AI engineering and AI governance.
How much do Data Engineer roles pay in the Netherlands?
Advertised salaries typically range from €5,000 to €6,900 per month, with the median around €5,500, based on 132 postings that quote pay per month.
Find your next Data Engineer role
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This guide is built from public job descriptions for Data Engineer roles classified as Core AI or AI-enabled. Skills, automation exposure and career directions are extracted from each job description and compared across the market. Postings are deduplicated, so a job listed on several boards or by several agencies counts once. Salaries are advertised ranges from the Netherlands, taken only from the 159 postings that quote pay. How we collect and deduplicate postings.