Analytics engineering emerged alongside the most significant technological revolution of the past decade – the rise of cloud computing. Today, we are experiencing even a greater transformation, one fueled by the meteoric rise of artificial intelligence (AI), which is reshaping how analytics engineers approach data challenges
AI has quickly become an integral part of the daily workflows for 80% of data professionals, up from 30% last year. It’s also changing how data teams work, with 70% of professionals now using AI to assist with code development, and 40% reporting that their data teams are growing. While investment in AI tools is leading the way, data quality remains a persistent challenge, with over 56% of practitioners highlighting it as a key concern.
These insights come from dbt Labs’ 2025 State of Analytics Engineering Report, the third edition of their annual publication, which dives into how AI is redefining data teams, where budgets are being prioritized, and why building trust in data is more critical than ever.
A key finding of the report is that AI is augmenting, not replacing data teams, as many had expected it to. Instead of replacing human expertise, AI adoption is changing how people work. It’s allowing professionals to spend less time on redundant tasks and focus more on specialized work. The report highlights that more than two-thirds (70%) of respondents use AI for analytics development in some form.
The increasing investments in AI tools to support data teams foster a more positive perception of their contributions. As a result, 75% of respondents agree that their organizations highly value and trust their data teams.
“AI is disrupting the way that teams work with organizational data,” said Mark Porter, CTO of dbt Labs. “As companies increase AI investments, leaders are prioritizing the teams responsible for data quality and governance—the essential foundation for AI effectiveness.”
“At the same time, data engineers are turning to AI to automate routine tasks, completely changing how data is delivered to the business. Because of this, the strategic role of the data team continues to grow, with AI as the catalyst. It’s a symbiotic relationship – data professionals make AI better, and AI makes data teams better.”
Analytics engineering is growing beyond tech, with highly regulated industries like finance (15%) and healthcare (10%) adopting it to manage complex, compliance-heavy data. Tech remains the largest sector at 34%, though its share has declined by 3% this year.
According to dbt Labs, organizations are investing in data again after a cautious period. AI tools are the top priority, with 45% planning to spend more on them in the next year. Data quality and observability come next, with 38% focusing on solving key data challenges.
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AI tools lead investment priorities, with 45% of respondents planning to boost spending in this area over the next year. Data quality and observability follow, with 38% aiming to increase investment to tackle urgent data quality challenges. Several other reports have highlighted the urgent need for organizations to tackle data quality issues, and this was a recurring theme throughout this year’s dbt Labs report.
The top use cases for AI include code development (70%), followed by documentation (50%), and answering data questions with SQL generation (22%). The report reveals that data teams are relying on general-purpose LLMs such as OpenAI’s ChatGPT and Gemini.
However, because these tools are not tailored for specific analytics tasks, organizations are increasingly adopting specialized GenAI agents. Currently, 25% of respondents are using AI solutions built into their development tooling
The report’s findings also reveal that interest in semantic layers, tools that make data clearer and more structured, is also growing, with 27% planning to invest more in this area. There is also a greater focus on empowering nontechnical users to work with transformed, governed datasets, which could improve data efficiency – a key focus for analytics engineering.
There is a growing push to empower nontechnical users. Nearly 65% of respondents believe that enabling business stakeholders to create and work with transformed and governed datasets would significantly improve organizational data efficiency. However, this highlights a core challenge in analytics engineering: maintaining data integrity while ensuring broader accessibility.
dbt Labs CEO Tristan Handy
At the dbt Cloud Launch Showcase event in May 2024, dbt Labs CEO Tristan Handy, highlighted the impact of AI for data professionals. He said, “And while this cloud transition is still playing out, AI is going to be the next big change in our lives as data professionals. The changes we’ll see over the coming years will be just as dramatic as those we’ve seen play out over the past decade.”
dbt Labs specializes in analytics engineering and is well-positioned to provide insights into the evolving field. This year’s report is based on a survey of 459 data professionals, including individual contributors (70%) and managers (30%). Among the individual contributors, 48% were analytics engineers, 36% were data engineers, and 16% were data analysts.
Later this month, dbt Labs will host the 2025 State of Analytics Engineering Virtual Event. The event’s agenda will include discussions on the report’s key findings, along with broader strategies for building effective data organizations, integrating GenAI, and addressing ongoing industry challenges.
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