Data engineers face increasing workloads despite the adoption of AI tools
AI was supposed to lighten the load. Automate the boring stuff. Optimize pipelines. Eliminate repetitive steps. But here’s the reality: 77% of senior tech executives say data engineering workloads are getting heavier, not lighter. That’s a flaw in how we’re implementing tech.
Teams are using AI tools like patchwork. One for data ingestion, another for processing, a third for analytics. Each tool makes one task easier. But together? They create fragmentation, complexity, and more operational overhead. Instead of simplifying the stack, we’re multiplying the effort it takes to manage it. That’s what creates the paradox, doing more to achieve less.
Chris Child, VP of Product for Data Engineering at Snowflake, put it plainly. When data engineers work across disconnected systems, there’s more infrastructure to manage, more risk, and more room for failure. It’s like trying to run a city with five separate power grids that don’t talk to each other. You spend more time managing systems than serving outcomes.
AI is powerful. But without a plan to consolidate tools and minimize operational drag, companies end up creating bottlenecks where there should be breakthroughs. That’s not innovation, it’s inefficiency at scale.
The scope of data engineering work is rapidly shifting due to AI integration
Two years ago, the average data engineer spent less than a fifth of their time on AI-related work. Today? Thirty-seven percent. In the next two years, it’s projected to jump to 61%. So what’s changing?
This isn’t about writing more SQL scripts or tuning clusters. Those tasks are beginning to fade into the background. In their place, engineers are designing transformation pipelines powered by large language models. They’re setting governance rules to make sure those models operate safely. This transition shifts the role from data mover to system architect, from reactive support to proactive business enabler.
Think about how CFOs used to get financial forecasting. It meant extracting numbers from contracts and pairing them with structured revenue data. Often, it took legal teams hours just to prep the unstructured data. Today, engineers using tools like Snowflake Openflow can pull PDFs from cloud storage, merge them with financial records, and pump them straight into an AI model, real-time context, no manual input.
Chris Child underscored this shift. The value isn’t only in the speed. It’s in the evolution of the work itself. Engineers are no longer just coders, they’re stewards of trust, governance, and context across the organization’s data assets. That demands a new mindset. It’s not about fixing broken scripts. It’s about building systems that deliver trustworthy results, at scale, across business-critical decisions.
Executives need to see this for what it is: a shift in core capability that should elevate data engineering within the organization, not keep it buried in operations. This is the team laying your AI foundation. Recognize that, and the ROI follows. Ignore it, and you’ll struggle to scale.
Tool fragmentation undermines the efficiency gains promised by AI
Automation isn’t the issue. Execution is.
AI tools are delivering on their promises, task-level productivity is up. According to the same MIT Technology Review Insights and Snowflake survey, 74% of organizations report higher output volume, and 77% note gains in quality. But here’s where things start to fall apart: too many tools, not enough integration.
Organizations are deploying AI tools in silos. Engineers prototype quickly, yes. But moving those prototypes into production becomes a problem when the tools involved don’t work together. Integration requires manual effort, governance is inconsistent, and monitoring becomes reactive instead of built-in.
Chris Child, VP of Product for Data Engineering at Snowflake, made the challenge clear: fast prototyping leads to scaling friction. When you stitch tools together to get a working demo, you often skip vital steps like access control and data governance. Then when it’s time to deploy at scale, you find out the system isn’t production-ready. That delays rollouts, burns resources, and kills momentum.
This is where leadership needs to step in. Tool sprawl doesn’t self-correct. CIOs and CTOs have to invest in platforms that reduce the operational burden, not increase it. Platforms that are interoperable, support governance out of the box, and eliminate redundant infrastructure should be prioritized. Anything else just adds more work on top of already overloaded engineering teams.
If you want scalable AI, stop patching together tools and start consolidating around systems that were built with scale in mind.
Enterprises have a limited window, approximately 12 months, to deploy agentic AI responsibly
Agentic AI isn’t complicated to understand. These are systems that can make decisions and take action without human input. If implemented right, they offload repetitive tasks and free up time for strategic work. If done wrong, they can crash systems, expose sensitive data, or make unchecked decisions that ripple through your business processes.
According to recent data, 54% of organizations plan to deploy agentic AI within the next 12 months, and 20% already have. That creates both pressure and opportunity. The pressure is to move fast. The opportunity is to move smart.
Chris Child warned that without foundational requirements in place, agentic AI becomes a liability. Specifically, two things: governance and oversight. AI agents must inherit access restrictions and follow a defined data lineage. And humans still need visibility into every action being taken, especially once agents begin interacting with production data.
Skip those steps and you risk untraceable errors, data quality breakdowns, or worse, security breaches. Successful agentic AI deployment starts by automating low-risk tasks with clear boundaries. Schema drift detection. Transformation debugging. These are manageable, high-return workflows that help organizations build confidence in autonomous systems.
C-suite leaders need to set the tone. This isn’t a territory to delegate without oversight. Push for early deployment, yes, but only within architectures that are governed, observable, and secure. The timeline is tight, and so is the margin for error.
Strategic misalignment in the C-suite weakens AI implementation efforts
There’s a critical disconnect right now, one that’s stalling momentum inside organizations claiming to lead in AI.
Chief Data Officers and Chief AI Officers are aligned. They know data engineers are foundational to scaling AI systems. The data confirms it: 80% of CDOs and 82% of CAIOs say data engineers are integral to business success. That’s alignment with reality. But only 55% of CIOs say the same. That’s a problem.
This disconnect is costing companies time, resources, and progress. When executives at the infrastructure level don’t see the strategic value in data engineering, the result is underinvestment, fewer resources, less visibility, and minimal influence during key planning cycles. The teams responsible for building scalable, governed AI do not get the authority or support they need. As a result, AI stays stuck in early-stage pilots.
Chris Child, VP of Product for Data Engineering at Snowflake, pointed to the issue clearly. CDOs and CAIOs work side by side with data engineering teams. They see the scope, the orchestration, governance, and transformation work that enables large-scale AI. CIOs, meanwhile, often focus on broader tech stacks and don’t engage directly with these teams. They miss the strategic architecture work at play.
Closing this perception gap isn’t optional. Executives sitting above infrastructure and AI must align around one central fact: data engineering is not backend support. It is pivotal architecture. It needs direct investment, cross-functional strategy, and access to decision-making tables. Otherwise, AI projects will remain fragmented, and won’t scale when the board asks “Why haven’t we moved faster?”
Business acumen has become as critical as technical skills for data engineers
Data engineering is no longer just about writing code, or making pipelines run a bit faster. The nature of the role is changing fast. Now, the skill that separates high-impact engineers from the rest isn’t just technical, it’s business intelligence.
Chris Child emphasized that engineers who understand what matters to internal users, what drives decisions, priorities, and value, have a distinct edge. They don’t just answer technical questions. They build systems that solve real, evolving business problems. They aren’t waiting for specs. They’re anticipating needs, choosing the right metrics, and delivering insights that leadership can act on.
This moves the discipline closer to the core of the enterprise. Engineers with business acumen make more relevant choices around architecture, automation, and tooling. They design data flows with purpose, aligned to specific outcomes. For execs, it means measurable ROI, quicker adoption, and actual impact across revenue, productivity, or customer intelligence.
For companies with larger engineering teams, the immediate question is whether to train existing personnel, hire new profiles, or restructure. There’s no universal model. But the principle is clear: prioritize understanding of the business function first. Certifications and frameworks are useful, but a deep understanding of why the work matters will change outcomes faster.
This isn’t just a people issue. It’s a leadership call. Organizations that build this strength into their engineering culture will set the pace. Everyone else will be catching up.
Resolving the productivity paradox requires tool consolidation and strategic reorientation of data engineering functions
AI has created real gains, but the way organizations are implementing it is creating friction. Faster task execution is easy to measure. What’s harder to track, but just as critical, is the drag caused by fragmented tooling, limited governance, and misaligned priorities. This is the core of the productivity paradox: individual efficiency is up, but team velocity is down.
Solving it requires more than adding more AI tools. It takes slowing down long enough to align around the right architecture. That means consolidating tool stacks, eliminating redundant systems, reducing operational complexity, and focusing engineering energy on value delivery, not just infrastructure management.
Chris Child, VP of Product for Data Engineering at Snowflake, said it clearly: organizations need to choose tools that increase productivity and simultaneously reduce operational burden. Otherwise, teams stay stuck managing glue code and fragmented workflows rather than driving impact. You don’t get scaled outputs from engineers when they’re firefighting interoperability and compliance issues every day.
The timing is also tight. Within two years, engineers are projected to spend 61% of their time on AI projects. At the same time, 54% of organizations say they plan to deploy agentic AI systems within 12 months. Neither of those transitions work if engineers are still buried in low-leverage tasks.
For C-suite leaders, the decision is straightforward. If AI is part of your core strategy, treat data engineering as a strategic partner. Prioritize consolidated platforms over point solutions. Build governance into your architecture early. Elevate engineers out of maintenance roles and into system design and execution.
Teams that get this right will move quickly. The ones still operating complex, disjointed stacks will watch their pilots stall, and others pass them by.
The bottom line
AI isn’t a magic solution, it’s leverage. But leverage without the right structure creates drag, not velocity. If your teams are automating tasks but still buried in integration chaos, governance gaps, and shifting priorities, you haven’t solved the core problem. You’ve just moved it around.
Data engineers aren’t just writing code. They’re building the architecture your AI strategy depends on. Waiting to prioritize their workflows, tools, and influence is a risk you can’t afford, especially with 61% of their time expected to shift toward AI in the next two years.
This isn’t the time to scale complexity. It’s the time to scale clarity. Streamline tools. Bake governance into your systems. Align your leadership on the strategic role data engineers play. Your AI outcomes depend on how fast and how well they can execute.
Move deliberately. Consolidate now. And give your teams the space to lead, not just deliver.
