Although it’s a rewarding career, the job of chief data officer (CDO) comes with a long list of challenges, including massive data volumes and complexity, lack of high-quality data, privacy regulations, executive alignment, artificial intelligence (AI) and machine learning integration, and bridging the gap between technical data expertise and business objectives. What’s more, CDOs struggle to secure adequate funding and staffing to implement data initiatives effectively. AI data engineers may soon play a pivotal role in changing all that.
Thanks to AI, the skills gap may narrow very soon. Enterprises are rushing to adopt AI agents and digital teammates across different roles and functions in the organization. This includes future AI data engineers, who will work alongside human data teams to carry out tasks such as building data assets, investigating ongoing issues, and optimizing costs.
The Challenging Life of a Data Engineer
Data engineers face several obstacles, despite AI’s revolutionary promise. These include adjusting to changing AI-driven processes, protecting the integrity and quality of data, handling privacy and security issues, adhering to ethical guidelines, and successfully integrating AI technologies.
Data engineers’ work is labor-intensive, including pipeline design and testing as well as workflow monitoring and optimization. Because of the sheer amount and complexity of data, careful attention to detail is necessary, which frequently strains resources and reduces productivity. The constantly changing landscape of data governance and compliance further complicates the efforts. Data engineers also must handle data privacy issues and regulatory frameworks while making sure that data processing complies with legal and ethical requirements.
How AI Data Engineers Can Help
AI data engineers can help ease their burden: Data engineering and AI have a naturally symbiotic relationship. The use of AI enhances data engineering processes and skills and promotes efficiency and creativity by automating time-consuming, repetitive tasks and enhancing actionable insights. Agents and humans can work together to create a strong partnership that addresses the intricacy of contemporary data ecosystems and opens the door to important breakthroughs.
First, however, AI-powered data engineers must be able to do more than just take natural language query inputs and output SQL statements. Although AI can help create simple queries, AI data engineers must be able to produce more complex SQL statements to be helpful teammates. From raw data to transformations and semantic modeling to reporting, AI agents for data and analytics require a thorough awareness of the current underlying data assets. More significantly, when they reason and follow the chain of logic, they must be able to modify these assets.
For instance, an AI data engineer must assess the current semantic model to see if it can support the request when it is asked to add a new report to the dashboard. The agent must examine the transformation and raw data layers to determine what is accessible and what must be created or altered if it is missing dimensions or measures for the necessary report. Before delivering the report, it needs to research, create, test, validate, and get input from the human.
AI Data Engineers in Action
Although AI data engineers are capable of a wide range of activities and engineering tasks, their deliverables usually involve creating a new data asset or altering an existing one. The agent must be able to alter semantic models, extract-load pipelines, perform transformations, and, eventually, create dashboards and reports.
Most use cases that data teams are now working on – such as developing or editing reports and dashboards, optimizing searches from a time and cost standpoint, troubleshooting data issues, rewriting codebases, etc. – could be handled by agents. After receiving input from humans, AI data engineers could use reasoning and chain-of-thought processes to analyze semantic models, transformations, and the data itself before acting on the task at hand.
The result will be enhanced productivity that adds value to the company. Every data professional will advance thanks to AI: Junior data engineers will be able to take on more difficult jobs, while senior data engineers will be able to accomplish more in the same amount of time. Additionally, AI data engineers will allow team members with less engineering knowledge to participate in new areas, such as dashboard designers working on data transformation pipelines.
More Work to Be Done
To make this future possible, we still need to construct a lot of fundamental pieces. Some would necessitate the development of large language models (LLMs), the core enabling technology, while others would call for infrastructure that would allow AI agents to access, comprehend, and alter data assets.
Progress is being made already. For example, this January, OpenAI unveiled o3-mini, a small reasoning model that supports highly requested developer features. Complex AI agents that can perform complex activities, like altering the organization’s data assets, require this architecture and chain-of-thought methodology. Although this next frontier in AI is still in its very early phases, the rate of progress is quite promising.
Data Assets as Code: A Must for AI Agents
In recent years, there has been a huge tendency in the data and analytics area to apply software engineering techniques to data management. As a result, code-first workflows are now the most popular method for managing data assets. While code-first workflows offer numerous advantages to humans, including version control, CI/CD, and collaboration, they are a must for AI agents.
AI agents generate code as their output after consuming it as input. To manage transformations, semantic modeling, and BI-as-code data assets, code-first workflows will be the primary interaction layer between the agents and data tools.
AI agents will be able to work on several projects simultaneously. They will spin up containers, make changes to the software, conduct continuous integration (CI), create branches inside the version control system, and show the findings to people. During the code review process, humans will examine code modifications and offer comments. In this manner, new AI colleagues can be easily incorporated into data teams’ code-based procedures.
Workspaces for Agents
AI agents will require dedicated workspaces to carry out processes and API access to data systems, just as humans require laptops and data tools. AI data engineers will create code more quickly than current humans. The bar for infrastructure will be raised as a result.
Given that agents can function on thousands of threads at once, workspaces will need to scale up significantly at the beginning of a task. After agents have finished their work, the workspace should, on the other hand, scale down to zero. For AI agents, container provisioning speed will be critical.
Compatibility with Current Data Tools
Extract-load, transformation, warehousing, semantic modeling, cataloging, observability, visualization, and many more data tools are used by human data teams in today’s workflow. In these systems, agents will require a method for programmatically modifying the states of data assets. Data tools will interact with current version control and CI/CD systems to enable development-deployment cycles for both humans and bots on data teams, and coding will become the standard method for managing the state of data assets across the board.
The Future Looks Bright
AI promises to improve productivity and expedite procedures, but in order to reduce risks and maximize benefits, its deployment necessitates careful consideration of governance frameworks and best practices. To properly utilize AI to spur efficiency and creativity, data engineers must adeptly handle these obstacles.
By automating difficult processes like data intake, transformation, and code optimization, AI can help data engineers significantly in their day-to-day routine tasks. AI agents will also improve productivity and guarantee data accuracy by making difficult tasks like flattening complex structures and analyzing complicated data easier. These new agents will allow data engineers to concentrate on high-value jobs and spur innovation in data-driven decision-making processes by optimizing workflows and speeding up the delivery of meaningful insights. And for CDOs, AI-powered data engineering will play a central role in alleviating the talent gap and solving one of the most challenging parts of a highly rewarding job.