AI Data Engineer Overview

Overview

Built for complexity. Designed for autonomy.

The Osmos AI Data Engineer is your intelligent agent for solving high-scale, high-complexity data engineering problems. It autonomously builds execution-ready, reusable Spark notebooks, transforming fragmented datasets into pipeline-ready code—so you can scale faster, build better, and worry less.

From intricate transformations to massive data integrations, Osmos AI Data Engineers help you orchestrate your Fabric environment with precision, visibility, and speed.

🚀 What It Is

The AI Data Engineer is a purpose-built AI agent that generates thoroughly tested Python Spark notebooks tailored to your ETL and data engineering workloads. Whether working with relational databases, JSONs, or hundreds of interrelated CSVs, it autonomously engineers pipelines that are production-grade, versionable, and ready for reuse.

👥 Who It’s For

• Primary User Persona: Data Engineering Teams

• Secondary Personas: Data Services and Platform Engineering Teams

🎯 Key Use Cases

• Building Spark-based ETL pipelines for:

  • Relational data (SQL, warehouse exports)

  • CSV, JSON, XML, Parquet, and log files

  • Interrelated or hierarchical datasets

  • Huge files or complex schema mappings

• Automating workspace orchestration in Fabric or other lakehouse environments

• Generating repeatable and testable pipelines for ongoing data integration

🛠️ How It Works

1. Tell Your Data Engineer About the Use Case

Upload whatever you have—source files, schema docs, designs, prior instructions. The AI will use this context to understand your goal.

2. AI Builds a Purpose-Built Notebook

The AI Data Engineer:

• Samples and profiles input data intelligently

• Identifies transformation logic and schema alignment

• Writes Spark-based Python code with built-in test coverage

• Iterates on logic if errors are detected during validation

3. Oversee the Engineer’s Work

You stay in control:

• Monitor progress

• Validate the output

• Review or modify code

• Provide additional feedback or instructions

4. Integrate Into Your Workflow

• Schedule notebooks to run automatically

• Plug them into Fabric, Airflow, dbt, or your existing pipelines

• Store in Git for version control and long-term reuse

🧩 Key Capabilities

Capability	Description
Autonomous by Design	Just describe what you need; the agent handles data sampling, transformation, and validation.
Built to Adapt	Capable of transforming complex, nested, or massive datasets
Code You Can Trust	Fully tested and version-controlled Python Spark notebooks
Production-Ready Output	Ready for deployment into orchestration tools and lakehouse pipelines

⚙️ AI Decision-Making Logic

Much like a skilled mid-level engineer, the AI Data Engineer:

• Sample files and learns from structure and patterns

• Adjusts logic based on runtime errors and data anomalies

• Writes and executes test cases

• Reprocesses files until success

• Outputs clean, reusable, and reliable notebooks

The goal: execution-ready notebooks that can be plugged into your production systems with confidence.

🧪 Example Scenarios

Input Scenario	AI Engineer Outcome
Hundreds of CSVs needing to be merged and normalized	Consolidated schema, clean joins, Spark notebook ready for scheduling
Relational data dump + JSON blobs	Parsed relationships, flattened nested data, transformed into analytics table
Terabytes of log data from multiple sources	Partitioned and transformed Spark pipeline to generate usable summaries
Mixed XML + Parquet sources for ML preprocessing	Cleaned, reshaped training dataset exported via structured Spark workflow

Summary

The Osmos AI Data Engineer is where modern data engineering meets intelligent automation. Designed to tackle what traditional ETL tools can’t, it generates reusable, testable code you can trust—letting you move faster, break less, and deliver more.

-Focus on what matters most. -Let the AI handle the engineering.