The Impact of AI Copilots on Analytics Engineering

Speeded up coding time, reduced ticket sizes, and improved PR pickup times with an AI copilot. The numbers are there to prove it.

July 10, 2024
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 min read
The Impact of AI Copilots on Analytics Engineering

In the research paper "Impact of AI Tooling on the Engineering Workspace," the team at Jellyfish uncovers how AI tools are revolutionizing engineering. It is such an insightful read; you should check it out -> HERE.

As we have built the only AI copilot for analytics engineers, I hereby verify the research findings that a copilot can drastically increase productivity and reduce time spent on boring, mundane tasks. I’ve seen it firsthand with our own Paradime users, some of whom have seen up to a 40% productivity boost.

So, I don’t just believe it; I know that AI copilots like our very own DinoAI will take your analytics engineering to the next level. Otherwise, we wouldn’t have built it 😉 And don’t worry, DinoAI will NOT take your job as long as you learn how to work well with it 🦖 → [Check out our AI Copilot Starter Guide]

Here are my three key takeaways with examples of how AI copilots will impact analytics work.

1. Speeded up coding time and 10x-ed productivity

Research observation: Significant changes were observed in coding time fractions among copilot users, with an average decrease of 3% with individual decreases as large as 15%.

AI-powered tools like GitHub Copilot significantly reduce the time developers spend on coding, allowing them to focus on other essential tasks. This efficiency boost helps improve overall productivity and streamlines the development process.

Impact on analytics engineering

Quickly generate complex SQL queries for aggregating data across multiple dimensions, freeing time for further optimization.

  • Reduced manual coding: Copilots like DinoAI accelerate writing dbt™ models, translating SQL (from stored procedures and other legacy systems) to dbt™, and building data pipelines. Users spend less time writing tests, or writing documentation and understanding complex code is quick.
  • Focus on design: Analytics engineers can dedicate more time to optimizing data pipelines, increase performance, and reduce cost.

2. Reduced ticket sizes and cycle times

Research observation: Average ticket sizes decreased by 16%, with cycle times decreased by 8%, making work more efficient.

AI copilots contribute to more efficient task management by reducing the size of each Jira ticket and shortening cycle times. This aligns well with modern CI/CD practices, facilitating faster and more manageable workflows.

Impact on analytics engineering

A decrease in ticket sizes allows for faster iterations of dbt™ models, higher quality, and the reduction in cycle times speeds up deployment.

  • Smaller tasks: More manageable data pipelines for dbt™, and higher test coverage leading to higher quality code. Also, teams can do more valuable work with less resources as more time-consuming tasks are automated with AI copilot.
  • Faster iterations: Quicker development and validation cycles.

3. Changes in PR pickup times

Research observation: PR pickup times decreased by up to 33% in some companies, indicating reduced workflow bottlenecks.

Using AI copilots can help reduce PR pickup times, and therefore minimizing workflow bottlenecks while speeding up the development process. Faster PR reviews contribute to more efficient integration of new code.

Impact on Analytics Engineering

A decrease in PR pickup times mean teams can now deploy their changes faster and with higher quality, i.e. teams can respond to business changes faster. Teams can provide business insights to their end users in a timelier fashion, so they can act more quickly i.e. now analytics team is accelerating the entire organization.

  • Faster pickups: Faster data deployment → faster access to business insights → faster organization.
  • Reduced bottlenecks: High-quality metrics, KPIs, and dimensions are available 3x faster


Using AI copilots is pushing a shift in analytics engineering efforts towards even more strategic and growth-oriented work. AI copilots like DinoAI allows analytics engineers to focus on high-impact and more exciting work, even with stretched resources, rather than dull routine maintenance, and accelerate the their entire organization. Businesses, in return, can maximize the impact of analytics spend.

Time for you to embrace the Paradime shift. Schedule a call with the team and learn how to maximize the impact of analytics. Let’s go!

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