For a long time, the role of a Data Engineer revolved around a core mission: collecting, organizing, and making available the data necessary for organizations to function. The work centered on designing data pipelines, integrating heterogeneous sources, and setting up infrastructure capable of reliably transporting and storing information. Data management systems already existed, but their role was primarily to feed databases, reporting tools, and decision-making warehouses. At that time, the data engineer primarily acted as a technical architect responsible for ensuring the flow of data within the company.
But this approach is now reaching its limits in the face of the explosive growth in data volumes and the widespread adoption of artificial intelligence. Cloud computing, connected devices, digital platforms, generative AI, and real-time applications—organizations are now generating massive amounts of information that must be collected, processed, and analyzed at high speed. According to IDC, the global volume of data is expected to exceed 175 zettabytes, illustrating the scale of the challenges involved in managing and leveraging it1.
At the same time, expectations regarding data infrastructure are undergoing a profound shift. Companies are no longer looking solely for systems capable of storing data. They expect platforms capable of powering artificial intelligence models, providing real-time analytics, and supporting data-driven strategic decisions. The rise of generative AI and agent-based AI further accentuates this need, as the performance of these models depends directly on the quality, availability, and governance of the data that powers them.
The figures illustrate this structural transformation:
- Every day, companies generate massive amounts of data from business systems, cloud applications, connected devices, and digital platforms.
- More than 80% of artificial intelligence projects encounter challenges related to the quality, accessibility, or organization of the data used to train and deploy models.
- The demand for real-time processing and advanced analytics is growing rapidly as a result of advances in AI, automation, and digital services.
In light of these developments, a paradigm shift is needed. Data engineering can no longer be limited to building technical pipelines and managing storage infrastructure. It is evolving to become intelligent, automated, and data-driven. Artificial intelligence is gradually emerging as a key driver for optimizing data flows, improving data quality, automating certain operations, and enhancing the performance of data architectures.
The profession is thus entering a new phase of transformation. The role of the data engineer is no longer limited to moving and storing data. Data engineers are becoming key players in the digital ecosystem, at the heart of projects involving data management, artificial intelligence, generative AI, and agent-based AI.
The rise of data-driven architectures is profoundly transforming the methods, skills, and tools used by data engineers.
How AI Is Transforming Data Engineering Practices
Artificial intelligence is not only transforming the models and applications that leverage data; it is also fundamentally redefining the way data is collected, prepared, governed, and distributed within organizations. Historically, the role of the data engineer centered on designing data pipelines, integrating multiple information sources, and maintaining infrastructure capable of feeding decision-making systems. Tools already existed to automate certain tasks, but the bulk of the work relied on technical development, predefined rules, and constant human oversight. With the rise of artificial intelligence, cloud architectures, and data-driven environments, a growing portion of these activities is now automated, augmented, and driven by intelligent systems. Data engineers thus operate in an environment where platforms can detect anomalies, optimize data flows, and automatically improve the quality of information, profoundly transforming professional practices.
This trend is evident at several key levels of data management and operations.
- Data Quality Automation: Traditional systems relied on static rules to identify errors or inconsistencies. AI now makes it possible to automatically detect anomalies, duplicates, or discrepancies in datasets. It improves the reliability of the information used by analytical systems and AI models. According to Gartner, poor data quality still costs large organizations an average of several million dollars a year2.
- Intelligent Optimization of Data Pipelines: Modern platforms use artificial intelligence to monitor data flows, anticipate slowdowns, and automatically adjust the resources needed for data processing. This capability improves performance and reduces service interruptions.
- Automated Data Infrastructure Management: Cloud architectures now incorporate intelligent features capable of optimizing storage, resource allocation, and energy consumption. Data engineers oversee increasingly autonomous environments capable of adapting to fluctuations in workload.
- Faster Data Integration: AI facilitates the connection between heterogeneous systems by automating certain data transformation, normalization, and mapping tasks. This automation significantly reduces the time required to implement data projects.
- Strengthening Governance and Data Management: Intelligent systems can automatically classify data, identify sensitive information, and verify compliance with internal policies and regulations. This capability is becoming essential in a context marked by the continuous growth of data volumes.
- Training Generative AI and Agent-Based AI Models: The performance of new artificial intelligence systems depends directly on the quality of the data used to train them. The Data Engineer plays a central role in preparing, organizing, and securing the data used to train and operate these models.
These changes are profoundly altering the nature of the profession. Data engineers are no longer limited to building pipelines and managing technical platforms. They must now interact with intelligent systems, oversee automated infrastructures, and ensure that data flows reliably within increasingly complex digital ecosystems. Data engineering is thus becoming a hybrid discipline, at the intersection of data management, artificial intelligence, and advanced digital architectures.
A New Role for the Data Engineer
The growing integration of artificial intelligence into digital infrastructure is not only transforming data management tools; it is also fundamentally redefining the role of the data engineer within organizations. Long viewed as a technical expert responsible for building data pipelines and maintaining integration platforms, the Data Engineer is now becoming a strategic player in the data and AI ecosystem. Their role is no longer limited to simply moving data around, but also involves ensuring its quality, governance, and availability to power increasingly intelligent and autonomous systems.
In a landscape marked by an explosion in data volumes, the widespread adoption of the cloud, and the rise of generative AI, the value of a data engineer now lies in their ability to structure, secure, and optimize complex information flows. The challenge is no longer simply to connect databases or develop high-performance pipelines, but to ensure that the data used by AI models is reliable, consistent, and usable at scale.
This trend has led to several major changes in the industry.
- Data-Driven Infrastructure Architect: The Data Engineer designs architectures capable of collecting, processing, and distributing massive volumes of data from multiple sources. He or she builds scalable platforms tailored to organizations’ growing needs in analytics and artificial intelligence.
- Smart Pipeline Supervisor: Modern systems incorporate automation mechanisms capable of optimizing workflows, detecting anomalies, and adjusting processes in real time. The Data Engineer oversees these environments to ensure their performance and reliability.
- Data Quality Guardian: Artificial intelligence models depend directly on the quality of the data that feeds them. The Data Engineer implements mechanisms for checking, validating, and cleaning data to ensure the integrity of the information used.
- A key player in generative AI and agent-based AI: The new generations of intelligent systems require structured, accessible, and well-governed data. The Data Engineer plays a direct role in preparing datasets, managing real-time data streams, and feeding advanced models.
- Head of Governance and Data Management: The proliferation of data and regulations is heightening the challenges related to traceability, security, and compliance. The Data Engineer helps implement policies that ensure the reliable and responsible use of data.
- Bridge between Business Units, Data, and Artificial Intelligence: The Data Engineer collaborates with data scientists, business teams, cloud experts, and AI specialists. He or she plays a central role in transforming operational needs into infrastructure capable of generating value from data.
According to an analysis by the World Economic Forum, jobs related to data, data engineering, and artificial intelligence infrastructure are among the fastest-growing professions worldwide, driven by the acceleration of AI projects and the digital transformation of businesses3.
As a result, the Data Engineer of the future will no longer be merely a specialist in data pipelines and technical platforms. They will become a strategic player in the data economy, capable of orchestrating intelligent infrastructures, feeding artificial intelligence systems, and ensuring a balance between performance, quality, and data governance.
What Skills Does a Data Engineer Need in the Age of Generative AI?
The fundamentals of the data engineer role—mastering databases, designing data pipelines, managing data infrastructure, and ensuring data quality—remain the essential foundation of the profession. The ability to reliably collect, transform, and distribute data remains at the heart of the practice. However, the rise of artificial intelligence, the widespread adoption of cloud architectures, and the development of generative AI and agent-based AI are significantly expanding the scope of skills expected. Data engineers must no longer simply build technical infrastructure; they must understand intelligent systems, orchestrate complex data flows, and ensure that the data feeding AI models is reliable, accessible, and properly governed.
This transformation is redefining training, professional conduct, and the culture of the profession.
Technical and digital skills
- Understanding Artificial Intelligence Systems Applied to Data: Data engineers must have a solid grasp of the fundamental principles of machine learning, generative models, and AI architectures in order to design infrastructures tailored to their needs. They must also understand the constraints related to training, inference, and the quality of the data used.
- Mastering Cloud and Distributed Architectures: Modern platforms rely heavily on cloud computing, distributed environments, and big data processing. Data engineers must be able to design scalable infrastructures capable of handling heavy workloads.
- Develop expertise in data management: The growing volume of data requires advanced proficiency in governance, cataloging, traceability, and metadata management.
- Automating data pipelines: Modern tools include advanced automation capabilities. Data engineers must know how to design, monitor, and optimize intelligent pipelines capable of adapting to changes in systems.
According to Gartner, the highest-performing organizations are investing heavily in data engineering skills to improve the quality of the data used in their artificial intelligence projects4.
Analytical and decision-making skills
The data environment is becoming increasingly complex and strategic.
- Maintain a critical mindset when dealing with automated systems: Intelligent tools can automate certain data transformation or validation tasks, but the data engineer must be able to assess the accuracy of the results and identify any errors.
- Understanding Business Needs: Data infrastructure must meet specific operational objectives. The Data Engineer must be able to translate business needs into appropriate technical architectures.
- Solving complex problems on a large scale: Modern systems handle massive volumes of data from multiple sources. Data engineers must develop strong analytical and problem-solving skills.
Ethical, Legal, and Regulatory Competencies
The widespread use of data and the development of artificial intelligence raise significant governance issues.
- Understanding Data Regulations: Data engineers must be well-versed in the requirements related to personal data protection, information security, and regulatory compliance.
- Ensuring data quality and traceability: AI models can only be reliable if the data that feeds them is properly governed. The data engineer plays a central role in ensuring this trust.
- Ensuring the responsible use of technology: Data management involves issues related to bias, fairness, and transparency in artificial intelligence systems.
Interdisciplinary and strategic skills
The profession is increasingly taking on a cross-functional nature.
- Collaborating with multidisciplinary teams: Data engineers work on a daily basis with data scientists, AI experts, business teams, cloud specialists, and data governance managers. The ability to communicate effectively with these different roles is essential.
- Understanding the Economic Implications of Data: Data infrastructure serves as a strategic lever for organizations. Data engineers must take into account performance, costs, and value creation.
- Supporting generative AI and agent-based AI projects: The Data Engineer is becoming a key player in the development of these technologies by ensuring the availability, quality, and security of the data required for their operation.
As a result, the data engineer of the future will not merely be an expert in pipelines and infrastructure. They will become a hybrid professional, at the intersection of data engineering, data management, and artificial intelligence, capable of orchestrating complex architectures while ensuring data quality, governance, and strategic value.
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Can artificial intelligence make data engineering more reliable?
One of the strongest arguments in favor of artificial intelligence in the field of data engineering is its ability to improve data quality, infrastructure reliability, and the efficiency of information flows. By processing massive volumes of data in real time—including system logs, databases, application flows, IoT data, and business data—machine learning models can identify anomalies, anticipate failures, and optimize the operation of data architectures. In an environment where organizations increasingly rely on data to fuel their analytics, AI models, and decision-making, these capabilities are profoundly transforming the way data infrastructures are designed and operated.
Specific examples:
- Advanced anomaly detection in data streams: AI-based systems continuously analyze data pipelines to identify inconsistencies, missing data, or unusual behavior. They enable the rapid detection of issues that could affect the quality of analyses or the performance of AI models.
- Improved Data Quality: Intelligent tools can automate the identification of duplicates, formatting errors, inconsistent values, or incomplete data. This capability enhances the reliability of the datasets used in data analysis, machine learning, and generative AI projects.
- Pipeline Performance Optimization: Artificial intelligence systems monitor data flows in real time and automatically adjust certain resources to improve processing times and minimize bottlenecks.
- Predictive Management of Data Infrastructure: By analyzing usage history and technical metrics, AI can anticipate certain failures or overloads. This approach helps improve platform availability and reduce service interruptions.
- Strengthening Data Governance: Intelligent systems can automatically classify data, detect sensitive information, and verify compliance with internal policies or applicable regulations. This capability is becoming essential in highly regulated environments.
The results are already becoming measurable. According to a Gartner study, organizations that invest in the intelligent automation of their data infrastructures are seeing significant gains in data quality, productivity, andoperational reliability.⁵ Artificial intelligence is thus emerging as a major driver for enhancing the robustness and efficiency of data-driven architectures.
However, these advances also bring new challenges.
- The Risk of Reliance on Automated Systems: Overreliance on automation mechanisms can reduce human vigilance and lead to certain errors or unusual situations being overlooked.
- The Opacity of Smart Systems: Some models used to monitor or optimize infrastructure can be difficult to interpret, making it harder to identify the actual causes of a malfunction.
- Data-Related Biases and Errors: Artificial intelligence can only be reliable if the data it uses is of high quality. Incorrect or incomplete data can lead to inappropriate decisions.
- Security and Compliance Challenges: The automation of processes and the centralization of data increase the requirements for cybersecurity, governance, and the protection of sensitive information.
Thus, artificial intelligence can significantly enhance the reliability of data engineering, but it does not replace the role of the data engineer. The most effective infrastructures are based on a synergy between automation and human expertise, where technology optimizes operations without replacing the analytical, oversight, and decision-making capabilities of data professionals.
What will the role of a data engineer look like in the future with AI?
The data engineer of the future will work in an environment where artificial intelligence systems are deeply integrated into organizations’ data infrastructures. Data platforms will become more autonomous, data volumes will continue to grow, and digital architectures will be increasingly distributed across the cloud, edge computing, and hybrid environments. In this context, the role of the data engineer will not disappear; rather, it will evolve into a role focused on orchestration, oversight, and strategic governance of data flows.
Several significant changes are already evident.
- The Rise of AI-Driven Data Engineering: Systems will incorporate advanced capabilities to automate pipeline management, optimize performance, and improve data quality. Data engineers will spend less time on repetitive operational tasks and more time designing strategic architectures and overseeing intelligent systems.
- The Emergence of Autonomous Data Platforms: Modern infrastructures will be able to automatically detect anomalies, adjust computing resources, and optimize processing without immediate human intervention. Data engineers will need to define operational rules, monitor these environments, and ensure their reliability.
- The emergence of new hybrid roles: The line between data engineering, artificial intelligence, and data management will become increasingly blurred. New roles will emerge, such as data engineers specializing in generative AI, architects of agent-based AI platforms, MLOps infrastructure specialists, and smart data governance managers.
- Large-Scale Data Security and Governance: Data will become an increasingly strategic asset. Data engineers will need to ensure its quality, traceability, regulatory compliance, and protection against security risks.
- Stronger collaboration between humans and intelligent systems: Tools will be able to recommend architectural optimizations, identify data quality issues, or suggest improvements to pipelines. Data engineers will play a central role in validating these recommendations and integrating them into complex environments.
- Greater integration at the heart of generative AI and agent-based AI projects: Artificial intelligence models will increasingly rely on reliable, up-to-date, and properly governed data. The data engineer will become one of the key guardians ensuring the smooth operation of these intelligent ecosystems.
According to the World Economic Forum, jobs related to data, artificial intelligence, and digital infrastructure are among the fastest-growing professions worldwide, driven by the accelerating use of AI across all industries6.
In this environment, the data engineer will no longer be merely a specialist in data flows. They will become a strategic player in the digital economy, capable of managing intelligent infrastructures, powering generative AI and agent-based AI systems, and aligning technological capabilities with organizations’ performance, innovation, and governance objectives.
AI-Enhanced Data Engineering: The Cornerstone of Data Trust
Artificial intelligence is profoundly transforming the way data is collected, processed, and utilized, but it does not change the purpose of the data. It accelerates data integration, automates certain transformation operations, and makes it possible to identify anomalies or inconsistencies that are difficult to detect by human means. It shifts business priorities: fewer repetitive technical tasks, more intelligent orchestration; less manual supervision, more workflow automation; less corrective maintenance, more predictive infrastructure optimization.
Yet, at the heart of this transformation, one constant remains: trust in data is still a deeply human trait.
Augmented data engineering does not mean the complete automation of data management. It relies on the synergy between algorithmic intelligence and the expertise of the data engineer. Artificial intelligence systems can detect anomalies, optimize pipelines, or recommend architectural improvements. But it is the data engineer who interprets these results, puts them into context, and adapts them to the organization’s business needs, technical constraints, and strategic objectives.
This distinction is essential. A data-driven decision is not limited to an algorithmic calculation. It affects the quality of analyses, the reliability of artificial intelligence models, regulatory compliance, and, at times, the company’s overall performance. It requires a detailed understanding of information flows, business practices, and the risks associated with data usage—all of which intelligent systems can only partially grasp.
From this perspective, the role of the data engineer increasingly involves ensuring the controlled and responsible use of artificial intelligence.
This includes, in particular:
- Rigorous validation of automated systems to ensure their reliability in complex and constantly changing environments.
- Continuous monitoring of pipelines and optimization models to identify deviations, biases, or performance losses resulting from changes in the data.
- Transparent data governance that ensures data quality, traceability, and compliance with regulatory requirements.
- Constant human oversight, particularly in critical environments where even the slightest data error can have significant consequences for decisions and artificial intelligence models.
The rise of augmented data engineering also opens up significant opportunities. It improves data quality, accelerates processing, enhances infrastructure performance, and facilitates the deployment of projects involving data analysis, generative AI, and agent-based AI. It contributes to better data utilization and a more efficient use of technological resources.
But this transformation goes far beyond the technical aspect. It raises questions about the role of the data engineer in an environment where certain decisions regarding architecture, processing, or optimization may be assisted—or even suggested—by intelligent systems. It leads to a redefinition of the role, no longer viewed solely as expertise in infrastructure and pipelines, but as the ability to manage complex systems with discernment, responsibility, and strategic vision.
In a world where systems are becoming capable of processing more data, faster and on a larger scale, the value of a data engineer will lie not only in technical expertise, but also in their ability to ensure that data remains reliable, actionable, and trustworthy.
Technology can automate data flows. The data engineer, however, must continue to ensure the quality of the data passing through them.
What if, ultimately, the true transformation brought about by artificial intelligence in data engineering were not to replace the expert, but to strengthen the very core of the profession—creating the conditions of trust necessary to transform data into knowledge, and then knowledge into value, in an increasingly complex digital environment?
Learn more
To broaden your perspective and understand how AI is reshaping other professions—from human resources to finance, and from healthcare to communications—we invite you to explore our dedicated section “AI & Professions”, which analyzes the concrete impact of intelligent technologies on skills, practices, and the organization of work.
References
1. IDC. (2023). Data Age 2025: The Digitization of the World.
https://www.idc.com/
2. Gartner. (2024). The Impact of Data Quality on Business Performance.
https://www.gartner.com/
3. World Economic Forum. (2025). Future of Jobs Report.
https://www.weforum.org/reports/future-of-jobs-report-2025/
4. Gartner. (2024). Top Trends in Data and Analytics.
https://www.gartner.com/
5. Gartner. (2024). Data Engineering and AI Automation Trends.
https://www.gartner.com/
6. World Economic Forum. (2025). Future of Jobs Report.
https://www.weforum.org/reports/future-of-jobs-report-2025/