Via 9to5google.com
The company's new BigQuery data engineering agent promises to eliminate the grunt work that consumes most of data teams' time, but Google is betting humans aren't going anywhere.
Google Cloud is rolling out an AI agent designed to let data teams build and manage pipelines using plain English instead of code, targeting the tedious preparation work that eats up the majority of their productive hours. At the same time, the company plans to hire hundreds of engineers to help enterprise customers actually use the thing.
What the Data Engineering Agent actually does #
The new tool lives inside BigQuery, Google’s cloud data warehouse, and functions as an autonomous agent that can construct, modify, and manage data pipelines based on natural language commands. Instead of writing complex ETL (extract, transform, load) scripts manually, a data engineer could theoretically describe what they need in conversational terms and let the agent handle the plumbing.
The preview launched on April 22, 2025, with general availability targeted for April 22, 2026.
Google Cloud has been expanding BigQuery’s AI capabilities across multiple domains, including data science workflows, conversational analytics, and observability tasks.
Data preparation—the cleaning, formatting, and pipeline management that precedes any actual analysis—reportedly consumes 60-80% of data teams’ productive hours.
Early adopter customers have reported a 90% reduction in ETL migration timelines using the new automation tools.
The hiring plan #
Google announced plans to hire hundreds of forward-deployed engineers, or FDEs, starting in 2026. These are technical specialists who will embed directly with enterprise customers to help them adopt Google Cloud AI products like Gemini and Vertex AI agents. Google Cloud leadership has been emphasizing that effective enterprise AI deployment requires technical resources that go well beyond what a traditional sales team can provide.
The competitive landscape for enterprise AI agents #
The enterprise data management space has become a battleground for agentic AI, with Microsoft, Amazon Web Services, and Databricks all investing heavily in similar capabilities.
The 90% reduction in ETL migration time reported by early adopters is a significant metric for enterprise procurement. ETL migrations are notoriously painful, often taking months and requiring significant engineering resources.
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