Data engineers: Ready your skills for the AI revolution ahead.


– Data engineers will play a critical role in the AI revolution and need to acquire new skills to enhance their career prospects.
– AI will transform data analytics in four key areas: building smarter data pipelines, less data mapping and more data strategy, up-leveling BI analysts’ game, and managing third-party AI services.

There is a misconception that the rise of AI will diminish the role of data engineers, but in reality, their expertise will be more important than ever. However, data engineers will need to acquire new skills to help their organizations get the most from AI and to enhance their career prospects for the future. AI presents an opportunity for organizations to extract more value from their data, but this cannot happen spontaneously. Data engineers will need to understand how and where to apply AI, which models and tools to use, and how to extract maximum value from data pipelines.

AI can greatly accelerate the process of extracting insights from raw, unstructured, and disorganized data sources. For example, by inserting AI into data pipelines, a data engineer can use a few lines of SQL to instruct an AI model to extract valuable insights from text files. This process would take many hours if done manually, and AI can uncover valuable insights that might not be found otherwise. Data engineers who understand how to apply AI models to extract maximum value from data pipelines will be highly valuable to their organizations.

Data mapping, which ensures consistency and eliminates duplicates in different data sources, can be a time-consuming task for data engineers. AI can be used to construct prompts that instruct the AI to create canonical databases, freeing up engineers’ time to focus on their organizations’ data strategies and architectures. This allows data engineers to understand all available data sources and how to leverage them to meet business goals.

Business intelligence (BI) analysts spend a significant amount of time creating static reports for business leaders. With the rise of AI-driven chatbots, executives will expect to interact with their reports in a more conversational way. This requires BI analysts to up-level their game and learn how to provide interactive capabilities in their reports. Cloud data platforms offer low-code solutions to extend the skills of BI analysts, but there is a learning curve in acquiring these new skills.

As AI continues to grow, data scientists will work more with third-party vendors that provide AI models, datasets, and other services. Data scientists need to be familiar with the options available, choose the right models for specific tasks, and manage the relationships with these third-party vendors. Acquiring these skills will be crucial for data scientists in the future.

The introduction of AI into data engineering will allow engineers to automate laborious tasks and focus on more strategic, proactive work. This will require new skills but will ultimately make their work more enjoyable and valuable to their teams.