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6 essential data engineer skills for modern data environments
As AI automates more tasks handled by data engineers, the role is shifting from pipeline building to strategic skills that keep modern architectures adaptable.
As AI technology takes over routine tasks, the role of the typical data engineer is pivoting from implementation to enablement.
As a result, data engineers who want to keep their skills relevant in the age of AI need to reconsider which types of expertise they prioritize. This applies to those just entering the field and seasoned data specialists who honed their expertise before the emergence of modern AI technology.
The changing nature of data engineering skills
Data engineers focus on building and maintaining the systems that move and process data. As automation takes over those routine tasks, success now depends on refining the higher-order skills that guide how those systems operate.
Historically, data engineers at most organizations handle three key responsibilities:
- Designing the data infrastructure and pipelines that businesses use to collect, process, analyze and manage data.
- Setting up that infrastructure based on those designs.
- Managing the infrastructure on an ongoing basis, including detecting performance issues when data moves across systems.
Designing, implementing and managing data infrastructure remain critical, but new technology -- especially AI -- has enabled organizations to offload parts of these tasks to software. Many of the implementation steps that data engineers once performed manually can now be automated. For example, after configuring the data infrastructure and pipelines, engineers can use tools to deploy them automatically. Additionally, some aspects of data infrastructure monitoring, such as detecting and correcting anomalies, can also be partially or fully automated.
Still, AI cannot design data infrastructure, pipelines or data products in most cases, so engineers remain essential. Automation also struggles with the delivery and management of complex data products, which continue to require human oversight.
6 skills for data engineers
An increase in automation doesn't mean that data engineers have become irrelevant, but they will need to develop skills in areas where AI falls short.
These six emerging data engineering skill areas define how data engineers can add value in modern data environments.
1. Optimizing real-time data processing and streaming
The ability to process streaming data in real time is essential for high-priority use cases, including fraud detection in finance, performance monitoring in IT and custom content delivery in retail.
Automation tools help stream data, apply transformations and run analytics in real time, but simply deploying a streaming pipeline doesn't guarantee real-time performance. Redundant data transformations, insufficient network bandwidth and overburdened processing infrastructure can slow data movement. Even brief processing delays can disrupt scenarios that require immediate analytics.
For this reason, troubleshooting and optimizing real-time streaming pipelines is a core skill for modern data engineers. They must recognize performance bottlenecks that prevent analytics and processing from occurring in real time, as well as how to mitigate those risks.
Because modern data streams are often highly automated, optimization has become even more important. When pipelines run autonomously to stream data, no human is available to detect and work around issues as they arise. Well-optimized design reduces the risk of problems.
2. API integration to enable data product delivery
Data product delivery is how businesses make data available to users. Automation technology enables the democratization of data delivery by allowing non-technical users to define their data needs and achieve their goals through self-service tools. Data engineers don't have to manually configure data products as often as they did in the past.
However, enabling data product delivery still requires integrating distinct systems through APIs. This is a task that data self-service tools can't always automate. It's also one that non-technical users can't handle on their own.
As a result, modern data engineers must be proficient in API integration to support data product delivery. They must identify and connect the correct APIs for custom requirements while managing the necessary integrations. They must also understand how to manage performance, security and privacy challenges during the implementation process.
3. Complex data monitoring and observability
Automation is also useful to monitor data infrastructure and pipelines for problems. Businesses can already detect most routine data issues, such as bottlenecks or errors within pipelines, with automatic alerts.
However, these tools often struggle to detect and remediate complex problems. For example, if an in-memory database experiences errors due to a RAM failure, automated monitoring tools can detect the issue but not the root cause.
To determine that hardware failure is the problem -- as opposed to other potential causes, such as bugs in the database engine or corrupt data -- a human would need to run diagnostic tests on the system's memory. Data monitoring and observability tools typically don't support hardware-level troubleshooting.
4. Working within a data fabric
Many businesses now maintain multiple disparate data platforms. To simplify access and management across diverse data assets, organizations deploy data fabrics. A data fabric is essentially a virtual, software-defined layer that unifies multiple underlying data systems, treating them as a centralized platform.
Data fabrics help businesses maximize the value of their data resources. However, deploying and maintaining the data fabric typically falls to data engineers. As a result, working with data fabrics has become a key skill. Data engineers must know how to:
- Integrate data systems to create fabrics.
- Optimize the performance of data fabrics.
- Manage access controls and security risks within data fabrics.
- Guide users without technical expertise to navigate data fabrics for self-service access.
5. Prompt engineering for AI-assisted data tools
Generative AI technology helps data engineers to complete tasks and configure resources without needing to write code. Instead, they give natural-language prompts to AI models describing what they want to happen. The models then generate the code or connect to the necessary data management tools to carry out the engineer's request.
However, the effectiveness of AI-assisted tools depends on the quality of the prompts. Data engineers should therefore develop strong prompt-writing skills. The most effective prompts are short and precise, since lengthier prompts can take longer to process. Good prompts also provide clear instructions that AI models can interpret, minimizing the risk of hallucinations.
In addition, because each AI model handles the same prompt differently, data specialists must learn to tailor prompts for the specific systems used by their organization.
6. Cross-functional collaboration for platform design
Data engineers no longer work in isolation. Instead, they are expected to collaborate frequently with other parts of the organization. These include other technical teams, such as developers and IT operations staff, and non-technical business users. Understanding stakeholder requirements is essential for designing effective data infrastructure.
For that reason, collaboration with other parts of the business has become another vital skill for modern data engineers. Success in this area relies on soft skills, such as clear communication, as well as proficiency with collaboration tools, including project management and UX testing software.
Chris Tozzi is a freelance writer, research adviser, and professor of IT and society who has previously worked as a journalist and Linux systems administrator.
