The Growing Importance of Data Engineers in Today’s AI-Driven Automation Landscape
From automated data products to AI-generated code, intelligent automation is reshaping the data engineering landscape. But rather than making data engineers obsolete, these innovations are highlighting their strategic role in ensuring data quality, scalability, and governance in modern data ecosystems.
Changing data engineering: towards self-engineering
Modern businesses treat data as a strategic asset - fueling innovation, operational efficiency, and competitive advantage. This value is unlocked through robust data products that collect, transform, and deliver high-quality data across the organisation. Traditionally, designing and maintaining these pipelines was the responsibility of the data engineer.
Today, the rise of tools like dbt, Infrastructure-as-Code, and generative AI is transforming this landscape. Data engineering is evolving into a new era of auto-engineering, where automation streamlines key tasks, but the strategic oversight of skilled data engineers remains essential.
The Evolution of the Role of the Data Engineer
The role of the data engineer isn’t fading—it’s evolving. No longer just a behind-the-scenes technician, today’s data engineer is stepping into a more strategic and visible position, serving as:
Architect of automated data systems
Supervisor of AI-generated data products
Guardian of data quality, compliance, and performance
Interpreter of business needs and translator of technical requirements
Automating does not mean blindly delegating
Automation brings clear advantages—faster development, increased productivity, and lower total cost of ownership (TCO). But it also introduces critical challenges, including:
Ensuring the quality and reliability of generated code
Aligning data workflows with evolving business requirements
Meeting non-functional constraints such as performance, security, and regulatory compliance
Even in automated environments, data products must be validated, documented, and continuously monitored. This is where the data engineer remains indispensable - providing the human oversight needed to ensure trust, consistency, and control.
Technologies that are redefining data engineering
Business Manifestos
Business manifestos are formal descriptions of functional requirements. Usable by automation tools, they allow pipelines to be generated directly from business objectives. However, their technical interpretation requires expertise possessed only by data engineers.
Modern Frameworks (dbt, Python, etc.)
These tools enable modularisation, increased collaboration, and easier versioning. The data engineer orchestrates their use and ensures consistency.
Self-Service Approach and Governance
Empowering businesses through simplified interfaces is promising. But it requires safeguards to avoid:
Duplication of indicators
Data silos
Non-compliance with security or compliance standards
Ephemeral and Governed Pipelines
Pipelines are no longer frozen in time. They are deployed on demand, used, and then deleted. This approach requires intelligent orchestration that only data engineers can ensure.
Building a Data Products Factory: A Systems Approach
The Dead End of Manual Data Products
Manually creating data products for each business need creates technical debt, redundancy, and a lack of governance.
The Data Products Factory: Industrialising Data
Keyrus offers a systemic approach, with three key steps:
Business Ideation Workshops
Creation of Structured Manifests
Automated Product Orchestration
These data products become natively documented, observable, and traceable, without manual additions.
Orchestrated automation, not blind automation
It's not about delegating everything to a generic AI, but rather combining:
DBT for transformation
Python for interpretation
Text2SQL for certain targeted automations
APIs for governance
All under the supervision of the data engineer.
Measurable benefits for the business
Acceleration and agility
Drastic reduction in production time
Freeing data teams from repetitive tasks
Better responsiveness to business needs
Reduced TCO
Ephemeral use of resources (temporary data products)
Optimisation of licenses, storage, and computing
Less maintenance effort
Native governance
Automatically generated data lineage, monitoring, and documentation
Enhanced consistency and continuous traceability
Optimised performance
Automatic aggregates via tools like Indexima
Better user experience and analysis fluidity
What AI does not replace: the essential missions of the data engineer
Even in an automated environment, certain responsibilities remain human:
Business interpretation: clarifying and completing manifests
Validation of generated pipelines: ensuring their robustness
Data modeling: creating reliable schemas
Continuous monitoring: adjusting and correcting flows
The data engineer becomes the guarantor of controlled automation.
A product-oriented and business-use approach
Keyrus proposes a methodological breakthrough:
No longer starting from raw data;
But from the expressed business need to generate the technical components, then automatically
This reverse logic refocuses data engineering on its purpose: creating business value, not just producing code.
Intelligent orchestration, driven by specialised AI
Keyrus has designed a metamodel of AI co-pilots, capable of:
Generate code
Document
Create tests
Monitor the entire stack
All driven by the business manifesto, which has become the single source of truth.
A field-proven approach
With more than 40 generative AI projects carried out, Keyrus demonstrates that this approach is:
Replicable
Scalable
Industrialisation-ready
It meets the expectations of decision-makers in 2025: rationalise costs, gain agility, and guarantee data quality.
Conclusion
Automating data engineering marks a significant leap forward in technology, but it doesn't eliminate the need for human expertise. Instead, it redefines the role of the data engineer. Today’s data engineers are not just builders; they are strategic thinkers, system architects, and guardians of data integrity. Without their guidance, automated data products risk becoming unreliable. With them, data infrastructure transforms into a powerful engine for business acceleration, innovation, and performance.
Ready to future-proof your data strategy? Discover how Keyrus can help you harness intelligent automation while keeping data quality and governance at the core. Contact us today to speak with our experts.