Building AI Products with More Effective Data Engineering Teams

AIQ Capability: Building AI Products

Data engineering teams are the heart of building AI products and stand at a critical juncture. The discipline itself—designing and building systems for collecting, storing, and analyzing data at scale—has evolved from a technical specialty into a strategic function that directly impacts business outcomes. Yet most organizations still treat data engineers as purely technical resources, missing the opportunity to unlock significantly faster project delivery and system efficiency gains.

The Business Case for Hybrid Skills

Organizations that invest in training data engineers in both technical and business skills see a 50% improvement in project delivery times (Future of Jobs Report 2025 | World Economic Forum; Opinion Paper: “So what if ChatGPT wrote it?” Mult...). This isn't a marginal gain—it's a fundamental shift in how quickly companies can turn data into actionable insights. The reason becomes clear when you consider what data engineers actually do: they design, manage, and optimize the flow of data within and between company systems.

When data engineers understand business context alongside their technical responsibilities—building and maintaining data pipelines that collect information from various sources and deliver it to centralized storage systems—they make better architectural decisions. They anticipate downstream analytical needs.

Breaking Down Silos Between Engineering Teams

The traditional separation between data engineers and application developers creates costly inefficiencies. Companies that foster collaboration between these groups achieve a 40% increase in system efficiency (Interventions to improve team effectiveness within...; AI in the workplace: A report for 2025 - McKinsey). This improvement stems from eliminating the handoff delays and miscommunications that plague siloed organizations.

Consider how data flows through most organizations. Application developers build systems that generate data, while data engineers build pipelines to collect and process it. When these teams operate independently, the result is predictable: data structures that don't align with analytical needs, performance bottlenecks that could have been avoided, and endless back-and-forth conversations to resolve integration issues.

Dedicated infrastructure facilitates access and control of data, which speeds up delivery of value. But infrastructure alone isn't enough. The teams managing that infrastructure need direct relationships with the developers creating the data sources. This means co-locating teams when possible, establishing shared success metrics, and creating forums for regular technical exchange.

Analytical Skills as a Strategic Differentiator

Technical proficiency in building pipelines and managing databases represents table stakes. Data engineering teams with strong analytical skills can drive more meaningful insights, leading to better business outcomes. 

Data engineers who understand analytical requirements can optimize data structures for query performance, anticipate the need for aggregations and transformations, and design systems that make it easier for analysts to extract insights. They think beyond "Can we store this data?" to "How will this data be used, and what architecture will best support those use cases (Could We Store Our Data in DNA? | The New Yorker)?"

This analytical orientation requires a different hiring and development approach (69 New or Updated O-RAN Technical Documents Releas...; Real-world gen AI use cases from the world's leadi...): 

• Look for candidates who ask questions about business context during technical interviews.

• Invest in training that exposes data engineers to the analytical tools and techniques their stakeholders use.

• Create rotation programs that embed data engineers with business intelligence teams.

The Path Forward

The median compensation for architectural and engineering managers stands at $167,740 per year, reflecting the strategic importance of these roles (Architectural and Engineering Managers - Bureau of...; What Can You Do With an Engineering Management Deg...). Data engineering managers who can build teams with the right blend of technical depth and business acumen will justify every dollar of that investment.

Start by auditing your current team's capabilities: 

• How many of your data engineers can articulate the business problems their pipelines solve?

• How often do they collaborate directly with application developers?

• What percentage of their time goes toward understanding analytical requirements versus just implementing technical specifications?

Then create development paths that reward both technical excellence and business engagement. The future belongs to data engineering teams that see themselves as enablers of business strategy, not just builders of infrastructure. Organizations that make this shift now will see the payoff in faster delivery times, more efficient systems, and insights that actually drive decisions.

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