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AI is increasing the need for engineers who can build trusted platforms, understand business context and combine technical judgment with AI-assisted ways of working.
September 10, 2026
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When I joined Publicis Sapient more than a decade ago, data engineering looked very different from what it does today. Much of my work focused on moving data from one place to another, building reports, maintaining data warehouses and making sure systems ran reliably.
Those fundamentals still matter. But over the years, I've seen data take on a much bigger role inside organizations. It is no longer only supporting business decisions. It is becoming the foundation for personalization, analytics and increasingly, AI-driven experiences.
Today, as a senior manager in data engineering, I help organizations build enterprise data platforms that can support those needs. Along the way, I have come to see data engineering not simply as a technology discipline, but as a business enabler.
One experience in particular shaped that perspective. My team and I were working on a challenge familiar to many enterprises: customer data spread across multiple systems, limited standardization and no single, trusted view of the customer.
The goal was clear: build a platform that could bring the data together and make it usable.
Over time, what started as a solution for a specific business need became something larger. The platform became a foundation for broader analytics, personalization initiatives and future AI use cases across the organization.
For marketing teams, it created a more unified and actionable view of customers. That meant they could build more relevant experiences, improve audience targeting and better understand campaign performance.
But the bigger lesson for me was: Great data engineering is not just about building technology solutions. It’s about creating platforms that can continuously adapt as business needs evolve.
What began as an effort to solve data fragmentation became a reminder that trusted, connected data can open up possibilities far beyond the original use case.
Now, data engineering is entering another important shift. This time, the catalyst is AI.
Across engineering teams, AI is becoming part of everyday delivery. Tasks that once required significant manual effort can now be accelerated through AI-assisted approaches, giving engineers more time to focus on problem solving, design and business context.
At Publicis Sapient, tools such as Sapient Slingshot are helping my team accelerate software development activities and improve engineering productivity. Sapient Bodhi is helping us explore agentic AI solutions that bring together enterprise knowledge, workflows and data to solve business problems in more connected ways.
For me, however, the most important shift is not only about the tools.
It is about how engineers think about their work.
For years, many engineering conversations I had focused on efficiency: How do we build this faster? How do we automate more? AI introduces another question: How can we use technology to create better business outcomes?
That shift from productivity to impact is what interests me most about the future of the profession.
My biggest AI lesson did not come from a major transformation project. It came from my own experience using AI tools.
As I began experimenting with them, I noticed that challenges which previously required significant time and effort could now be explored, tested and accelerated through simple conversations and prompts.
As a data engineer, that meant I could quickly compare design options, understand unfamiliar code, draft transformation logic, or think through edge cases before implementation.
That changed how I thought about the future of engineering. It was not going to be about humans versus AI. It would be about how effectively people learn to work with AI.
The future of engineering combines human creativity, domain understanding and engineering expertise with AI to solve problems in smarter and more impactful ways.
Future data engineers will still need strong foundations in data platforms, architecture, data modeling, data quality and governance. But technical expertise alone will not be enough. They will also need curiosity, adaptability, business understanding and the ability to work effectively with AI tools.
Just as important, they will need to know when to question an AI-generated output, validate it against trusted data and apply human judgment before using it to support a decision. The engineers who succeed will be those who can combine technical depth with context, collaboration and a clear understanding of the business value their work is meant to create.
At Publicis Sapient, we are moving from building data solutions to building intelligent ecosystems. The next generation of data engineers will create AI-powered capabilities that help businesses operate in more connected and responsive ways.
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