By Stijn Dolphen
Last week’s launch of dbt Copilot wasn’t just another product update - it was a glimpse into the future of analytics engineering. If you work with data, whether as an analytics engineer, data analyst, or data scientist, you've probably noticed how generative AI is making waves across the modern data stack. But what does this mean for your daily work? And how can you best prepare to leverage these advancements?
GenAI graduated from buzzword to essential a long time ago. It’s being embedded into every layer of the data stack. Companies are racing to integrate AI capabilities into their tools to increase efficiency or improve accessibility. From AI-powered coding assistants to natural language-driven business intelligence (BI) tools, the modern data stack is really changing on all fronts. This shift doesn't just change how individuals work, it impacts how businesses make decisions, structure teams, and allocate resources.
Why This Matters for Business Leaders
As AI capabilities become embedded across the data ecosystem, the business implications are profound. Enhanced automation means shorter development cycles. Improved accessibility to insights enables faster decision-making. And a more empowered data team leads to better alignment between analytics outputs and strategic goals. Leaders who embrace this change early can gain a significant competitive edge by building agile, data-driven organizations.
What You Can Do: Start by identifying repetitive tasks within your data workflows, like manual testing, documentation, or model building. Ask your data team where GenAI can help, and evaluate how AI adoption could accelerate business outcomes.
GenAI Taking Analytics to the Next Level
We’ve already seen this transformation with tools like GitHub Copilot. Initially designed for code completion, GitHub Copilot has since evolved into an intelligent assistant capable of answering programming questions and even reviewing code within a developer's specific framework.
Now, similar AI-driven enhancements are emerging in the entire analytics workflow. dbt Copilot represents one of the first major steps in bringing this level of AI-assisted efficiency to data modeling, testing, and documentation.
While AI assistants help engineers write and optimize code, BI tools are already undergoing a similar transformation. Conversational BI platforms allow users to ask natural language questions and receive immediate insights - no SQL expertise required. AI-assisted SQL and DAX query generation further streamlines data workflows, reducing the time spent writing complex queries and ensuring they are cost-efficient and performant. These developments free up time to focus more on deriving business value from data rather than fine-tuning syntax. For organizations, this means faster time-to-insight and more accessible data for decision-makers.
Why It Matters: When GenAI helps automate tasks like writing SQL or documentation, engineers and analysts can shift their focus to strategy, innovation, and collaboration with business teams—delivering value where it counts.
Beyond BI and analytics, data warehouses like Snowflake and Databricks are heavily investing in AI capabilities too. By integrating models like Mistral AI and acquiring AI-driven companies, these platforms are accelerating the adoption of GenAI features across the data stack. The fact that Snowflake's current CEO was the co-founder of Neeva, an AI-driven company they later acquired, shows their commitment to AI-powered transformation. All this really indicates that AI-driven data optimization and transformation are set to become standard elements of the analytics workflow.
How Will GenAI Change the Day-to-Day of an Analytics Engineer?
So, what does all this mean for analytics engineers? Simply put, AI will be integrated even deeper into every layer of the modern data stack. dbt Copilot is a prime example of how AI can become an integral part of a product ecosystem, helping analytics engineers automate tedious tasks like:
- Generating tests and documentation
- Creating semantic models using natural language
- Enhancing metadata generation for better collaboration
By embedding AI directly within the dbt ecosystem, dbt Copilot eliminates many manual processes, allowing engineers to focus on higher-value work. To quote dbt Labs themselves: with a human always in the loop, your expertise remains at the core. For companies, this means that technical teams can focus on impact instead of repetitive tasks, accelerating project timelines and reducing operational friction.
Try This: Use natural language prompts in dbt Copilot to document a model or generate a test. Start building your own list of effective prompts to reuse and share within your team.
For analytics engineers to make the most of these AI advancements, the focus should be on leveraging business context as well as creating standards and best practices. GenAI is most effective when combined with strong frameworks, well-defined rules, and robust tooling.
The introduction of dbt Copilot is just the beginning. As AI continues to evolve, analytics engineers will see even greater integration of GenAI into their daily workflows. The key to staying ahead will be understanding how to best apply these tools, ensuring that AI increases efficiency without compromising quality or business context. At Dataroots, we help organizations not only adopt these tools but do so in a way that aligns with strategic goals, ensuring value from both a technical and business perspective.
What You Should Do Next
- Analytics Engineers: Start experimenting. Test dbt Copilot’s capabilities, give feedback, and think critically about where GenAI adds the most value to your workflow.
- Business Leaders: Speak with your data teams about how they’re using GenAI today and what’s stopping them from scaling it. This is a great moment to invest in enablement and experimentation.
- Focus less on “what AI can do” and more on “how AI can make our work more impactful.”
Ready to Explore What GenAI Can Do for Your Data Team?
If you're curious how GenAI can supercharge your analytics engineering workflows, streamline BI processes, or enhance your modern data stack, we're here to help. Reach out to Dataroots to discuss how we can guide your business through this AI-driven transformation, with the right strategy, tools and impact-driven mindset.