Meet the Osmos AI Data Engineer: Code Generation built for data

Data Engineers are the unsung heroes of modern enterprises—building data pipelines, transforming datasets, and ensuring quality with precision. But too often, the job feels like Groundhog Day: read from Bronze, clean and transform, write to Silver. Rinse and repeat.

The real challenge? Scale.

From managing dozens of ERPs and vendor feeds to navigating sprawling data domains, data engineers require not only technical chops but also deep institutional knowledge. Every dataset is different. Every transformation is custom.

At Osmos, we’ve spent years helping companies like Corpay and Rakuten solve their most complex data challenges. Along the way, we realized something important: the modern data engineering team doesn’t need another drag-and-drop ETL tool.

They need help that thinks and works like a real data engineer.

Great data engineers are the rare earth elements of the digital age

Data Engineers are expected to update data systems to meet ever-evolving business needs without compromising quality. Seasoned data engineers build a deep understanding of how their data is organized and why it is organized in the manner that it is. They are not just code jockeys. They are expected to design data systems, build code, validate and deploy without losing a byte of data.

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The modern CTO’s conundrum

Today’s CTOs face relentless pressure to modernize their data stack, integrate AI, and ship faster. But team growth can’t keep pace with demand.

Yes, code generation tools exist—but they’re not designed for data teams. They lack context from data estates and don’t integrate with enterprise environments. The result is tension because CTOs expect their investment in AI tools will result in more productive teams and those teams are unable to meet expectations because the tools aren’t built for data needs.While code generation tools are available, they fail to address the specific requirements of data teams. These tools lack the necessary understanding of data environments and do not seamlessly integrate with enterprise systems. Consequently, a disconnect arises as CTOs anticipate increased productivity from AI investments, but data teams struggle to meet these expectations due to the tools' inherent limitations for data-centric tasks.

What data engineering teams really need is an AI that understands how they work—and writes production-grade code that fits seamlessly into their stack.

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What are Osmos AI Data Engineers?

The Osmos AI Data Engineer is an agentic AI built specifically for enterprise data teams. It understands your goals, designs intelligent solutions, and automatically generates the code to power your pipelines—no drag-and-drop shortcuts, no oversimplified abstractions.

Data Engineer acts like a precocious, enthusiastic software engineer who’s always ready to take on all tasks big and small. Just tell it what you want, your Data Engineer asks a few clarifying questions based on its knowledge of your workspace and gets to work. It will write code, perform validation and write to production tables upon your approval.

It’s context-aware, collaborative, built for your real-world data complexity and always ready to help.

The Osmos Engineer Builds Fabric-Native AI Notebooks

The Osmos AI Data Engineer creates Fabric-native PySpark notebooks that are:

• Fully integrated with OneLake and supports formats such as CSV, Excel, JSON, Parquet and .txt
  
  
• Aligned with your business logic and transformation goals
  
  
• Built with structured sections: inputs, exploration, transformations, validations, outputs
  
  
• Equipped with flags for test vs. production mode
  
  
• Embedded with row-count tracking, metric logging, and version control
  
  

And yes—it writes, tests, fixes, and finalizes its own code.

You can schedule, monitor, and manage these notebooks just like any other Fabric workload.

Trust through observability and control with Osmos

Human Data Engineers often build mission-critical code and therefore trust and transparency are non-negotiable. Data Engineers are expected to document and validate their work. Furthermore, good quality code offers traceability and is easy to update. Due to the mission-critical nature of their work, trust and transparency are essential for human Data Engineers. They are expected to thoroughly document and validate their code. High-quality code also provides traceability and facilitates updates. Your Osmos AI Data Engineer works to the same high standards as your most trusted team members.

You can trust your Osmos AI Data Engineer because it operates with full transparency by offering:

• Audit trails for every notebook generation and update
  
  
• Version-controlled instructions and transformation logic
  
  
• Row-level metrics and historical run tracking
  
  
• User-driven edits through code or updated instructions
  
  
• Execution within your Fabric workspace—no data leaves your tenant

It creates versioned Python notebooks. Those notebooks are hosted inside your Fabric workspace and use your tenant’s compute ensuring that no data leaves your organization’s trust boundary. Those notebooks are automatically validated with test results available for your inspection.

You’re always in charge. And when business logic evolves, you can tell your Data Engineer to update the necessary code.

AI Engineers for Every Use Case

You can create separate Osmos AI Data Engineers to  meet the needs of each distinct use case within your workspace. For example:

• One may handle ERP transformations for Finance
  
  
• Another may manage IoT ingest and cleanup for Manufacturing
  
  
• A third may own Sales and CRM enrichment pipelines
  
  

This structure enables:

• Clear separation of concerns
  
  
• Context reuse across notebooks
  
  
• Effortless scaling without losing visibility

Deeply Integrated with Microsoft Fabric

We built the AI Data Engineer to work where your data already lives.

• Notebooks execute on Spark pools inside your Fabric tenant
  
  
• All transformations happen within your data boundary
  
  
• Integrates natively with OneLake, Power BI, and Microsoft scheduling capabilities
  
  
• Certified to Microsoft’s standards—no data copies, no external systems
  
  

This is not a parallel platform—it’s an intelligence layer embedded into Microsoft Fabric.

Put Your First AI Data Engineer to Work

We’re already working with enterprise customers across manufacturing, finance, CPG, and healthcare. The results are real:

• Onboarding massive ERPs in days, not quarters
  
  
• Empowering engineers with tools they already know (Fabric notebooks, GitHub)
  
  
• Replacing brittle ETL logic with durable, AI-generated pipelines
  
  

If you're ready to simplify your data operations, we’d love to show you what the AI Data Engineer can do.
→ Introducing Osmos 3.0

We’d love to show you what autonomous data engineering looks like. Get in touch to book a demo

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