Shridhar Iyer, senior technical lead director of data governance, joined Meta because he believes data engineering is about more than building data — it’s about shaping products and driving real-world impact.

“Data engineering is a core pillar of Meta technologies, which makes a big difference. Among industry peers, I often hear about data engineers being treated as a support function or shoe-horned into limited roles. Here, we have a real seat at the table. We make decisions and work on projects that directly impact people. This connection is why I got into data engineering in the first place.”

Beyond giving his own work meaning, Shridhar says this level of product ownership accelerates how data engineers grow their skills.

As Meta scales, creative problem-solving flourishes

Shridhar has spent more than a decade at Meta. During that time, he’s seen the data engineering role evolve dramatically. What began as a small, centralized function has grown into a distributed discipline that touches nearly every area of the business.

He points to the company’s push to establish shared schema and business logic across technologies — including Facebook, Messenger, Instagram, WhatsApp and Threads — as a defining learning moment for data engineers.

“Our product ecosystem has expanded massively over the past 10 years,” he explains. “That growth has changed the kinds of questions we ask and the complexity of the problems we solve. We have had to evolve many principles in parallel, from data efficiency and capacity to privacy, machine learning observability, and accessibility. And as Meta scales, the focus has shifted from simply building infrastructure to enabling accessible product analytics. This requires schema discipline, semantic consistency, and trustworthy definitions that make data understandable and comparable across surfaces. When those foundations are in place, anyone can derive meaningful insights and make better product decisions, even without deep data expertise.”

Along the way, Shridhar’s team has adopted software engineering best practices — such as reusable abstractions — to unify data across disparate technologies and better understand how people connect at scale.

This responsibility, he says, requires a surprising amount of creativity on data engineers’ parts.

“Meta technologies are deeply interconnected and function at enormous scale, which adds a unique layer of complexity to otherwise common tasks like reporting or dashboarding. This forces us to think differently, invent new approaches and continuously stretch how we solve problems.”

How AI is transforming data engineers from doers into ideators

Scale isn’t the only thing that’s changed how Meta approaches data engineering. AI is redefining the role as well, shifting engineers from hands-on operators to strategic thinkers who design how data creates meaning.

“The heart of our work today is to give AI context and teach it how to reason over data accordingly. Traditionally, we worked in very fixed ways — schemas, APIs, deterministic systems. Now, the stack has evolved to natural language and text, with intelligence capable of interpreting and reasoning in entirely new ways. It’s a fundamental shift.”

Over the past year, much of Shridhar’s work has centered on building AI agents for data analytics and experimenting with ways to provide AI with the context it needs to generate accurate insights.

“We’re treating context as a first-class asset — something that can be version-controlled, tested, deployed and evolved at scale,” he explains.

He describes this shift as moving up an “intelligence pyramid.”

“At the bottom is raw data. As you move up, you add meaning — which is where the industry has focused for the last several years. With AI, we’re climbing another level, using machine intelligence to reason over that meaning and surface insights.”

Looking ahead, Shridhar believes data engineering will become increasingly conceptual.

“In the past, we’d define a goal, build the physical data structures and then analyze them. Now, the role is flipping. Data engineers will focus on defining context and intent, while AI agents generate the tooling needed to extract insights.”

Growing skills through product impact

Reflecting on the many people he’s worked with throughout his time with Meta, Shridhar believes that professional growth and change are integral parts of the company culture across every team, not just data engineering.

“Meta is unique culturally, and that’s a big reason why I’ve stayed. We encourage people to ideate and build in every direction — bottom-up, top-down and across teams. It’s a multidirectional environment where ideas are challenged thoughtfully, no matter where they come from.”

That culture, he says, has been one of the biggest drivers of his own professional development.

“I’ve gained deep technical skills here, but the most significant growth has been in how I think and communicate. You learn how to articulate ideas clearly, defend your reasoning and collaborate across disciplines — skills that make you a better engineer and open doors to many different paths.”

For Shridhar, it all boils down to making an impact with your work. It’s why he first joined Meta in 2013, and it remains a core reason why he stays at the company today.

“During orientation on my first day at Meta, I got to meet with some of the highest levels of leadership and feel like my ideas would be taken seriously. When someone like Chris Cox, the Chief Product Officer at Meta, takes time to meet with new engineers, it sends an important signal. I knew right away that I was exactly where I needed to be.”