Bridging the data engineering skills gap: Build data pipelines with agents, SDKs and automation

The era of intelligent agents is here. Across industries, organizations are experimenting with AI-powered agents to automate workflows, assist decision-making and accelerate business outcomes. These agents have the potential to dramatically improve productivity and profitability, but only if they have access to clean and high-quality data.

That’s where many organizations are hitting a wall. The data foundation required to support agents - reliable pipelines, unified integration frameworks and governed access - is being strained. Technical debt, tool sprawl and lack of visibility into pipeline performance already make it difficult to operationalize analytics. Factor in a global IT skills shortage, and the challenge becomes existential.

Indeed, by 2026, International Data Corporation (IDC) predicts that 90% of global organizations are shrinking even as data demands grow. Without sufficient expertise to maintain pipelines and adapt to new architectures, organizations risk stalling their before they even begin. Agents can’tand right now, too few teams can deliver it.

The skills gap doesn’t exist in isolation, it amplifies every other issue in the data system. As data landscapes evolve, teams often rebuild pipelines from scratch whenever storage or architecture shifts, adding to technical debt. Tool sprawl fragments data integration across incompatible systems, requiring niche expertise to manage. Visibility into pipeline performance remains limited, making quality issues hard to detect before they impact downstream applications.

These challenges compound under workforce constraints. Even when organizations have the right strategy for data-driven transformation, execution slows down because too few people have the technical depth to turn intent into production-grade pipelines.

Meeting this challenge requires reimagining data integration with flexibility. To build a strong data foundation, organizations need to empower every type of user to work in the way that fits their skill set and goals. This approach includes every professional, from business analysts to seasoned data engineers.

For some, that means agentic data integration, where natural-language intent drives pipeline creation. For others, it means programmatic integration through a Python SDK, giving technical teams fine-grained control, automation and reproducibility. And for those users who prefer a visual experience, a drag-and-drop interface with prebuilt connectors remains a powerful option for accelerating work.

The result is faster development cycles, fewer dependencies on specialized roles and greater consistency across the organization. The flexibility across these authoring modalities bridges the gap between what teams want to do and what they have the skills to deliver.

Different users need different levels of control and automation. An ideal data integration framework provides a common foundation across all authoring styles. Business users can iterate in natural language, analysts can visualize flows and engineers can automate with code - all within the same offering. By bringing these experiences together, data becomes more accessible, and teams become more collaborative.

This is exactly the approach behind IBM^® watsonx.data integration - designed to operationalize the future of data engineering. Watsonx.data integration brings together agentic, visual and programmatic authoring styles, giving every user, from business analysts to data engineers, the tools to build and govern trusted data pipelines.

1. Agentic integration (in preview): Ideal for line-of-business users who need rapid access to insights, preferably in a self-service manner. Agents convert intent into executable data flows, lowering the barrier to entry and reducing cycle times from weeks to minutes. Sign-up for the agentic data integration design partner program.
2. Unified Python SDK: Designed for technical users who prefer a code-centric approach. The software development kit (SDK) provides full programmatic control, enabling teams to build, test and deploy pipelines as software. With configurations centralized in code, data engineers gain fine-grained governance, reproducibility and automation across environments. Maintenance and upgrades become faster and more predictable, reducing operational overhead.
3. Visual drag-and-drop interface: For teams that favor low-code development, the canvas-based UI offers prebuilt connectors and transformations. This approach streamlines development while preserving visibility and governance. It’s an access method for teams scaling data integration without requiring advanced programming expertise.

Underpinning these experiences is a unified control plane that minimizes tool sprawl and decouples authoring from execution. The watsonx.data integration unified control plane also supports batch, streaming, replication and unstructured data integration pipelines - all with built-in data observability and hybrid deployment flexibility.

By combining agentic intelligence with deep integration capabilities, IBM’s approach helps organizations modernize data operations and close the skills gap without increasing headcount.

The global skills shortage is real, but it doesn’t have to be a constraint. With agentic data integration, enterprises will be able to transform how work gets done - enabling everyone from business users to seasoned data engineers to build, automate, and scale data pipelines with confidence.

IBM watsonx.data integration represents this new model: intelligent, flexible and inclusive by design. It’s not just about integrating data. It’s about integrating people, tools and intent into a unified, scalable framework - so organizations can focus on innovation, not limitations.

Watch the bridging the data engineering skills gap webinar