# Data Engineering Agents in 2025: From Midnight Firefights to Everyday Copilots

Karolína Everlingová  
Product Marketing Manager  
29 Aug 2025 · 11 min read

**Data engineering has long been a grind. Pipelines stitched together by hand, SQL queries tuned one at a time, integrations glued with fragile scripts. Engineers carry pagers for 2 AM failures, analysts wait days for queries, and new hires spend weeks decoding undocumented jobs.**

**The result?** Slow delivery, hidden tribal knowledge, and mounting pressure as businesses demand faster insights.

Enter the **Data Engineering Agent** — AI copilots embedded in platforms like **Keboola**, **Snowflake**, **Databricks**, **Matillion**, and **Boomi**. They promise to **turn natural-language requests into reliable pipelines, SQL, and docs** — all inside governed environments. No shadow AI, no shortcuts, just faster delivery with control.

This isn’t about replacing engineers. It’s about changing the rhythm of work: from firefighting and manual toil to guided collaboration, where AI clears the grunt work and humans focus on strategy, design, and quality.

**So what do these agents actually look like in practice? Let’s take a closer look at the leading players — one by one — and compare their strengths, focus areas, and use cases. Only then can we understand where they overlap, where they differ, and which might fit your team best.**

## Keboola Data Agent: Turning Chaos into Collaboration

**We’ll start with Keboola, one of the few platforms trying to cover the full spectrum — from pipelines to analytics — with governance at the core.**

Picture the all-too-familiar scenario: a mission-critical pipeline fails overnight. Logs are cryptic, Slack is on fire, and the CFO’s dashboard is red.

**Keboola Data Agent** turns that nightmare into a quick conversation. Ask: “Why did the Salesforce-to-Snowflake pipeline fail yesterday?” The agent reads the logs, identifies the error, and even drafts a safe fix in dev. Documentation updates automatically, capturing the root cause so tribal knowledge doesn’t disappear.

But it’s not just a firefighter. When you need new pipelines, just describe them: “Ingest HubSpot and Salesforce, join by company ID, calculate CAC by cohort, refresh weekly.” Minutes later, a full pipeline is ready for review — transformations, schedule, lineage. Analysts can ask questions directly (“What’s Q3 revenue by region?”) and get charted results with SQL included.

**Why it matters**: Keboola’s agent combines **breadth (pipelines, debugging, docs, analytics)** with **deep governance** — every action is versioned, approved, and auditable. It empowers engineers and analysts, collapsing silos into one governed workspace.

## Snowflake Copilot: Unlocking Self-Service Analytics

**While Keboola emphasizes end-to-end pipelines, Snowflake has taken a different approach: keeping it simple, focusing on SQL, and doubling down on analyst productivity.**

In many organizations, SQL is the bottleneck. Analysts have ideas but queue requests; engineers spend cycles translating questions into queries.

**Snowflake Copilot** closes that gap. Within Snowflake Studio, business users can ask: “Show Q3 revenue by product compared to last year.” Copilot generates SQL, executes it, and shows results. Follow-ups refine the query conversationally. It explains code, suggests optimizations, and answers syntax questions directly from Snowflake docs.

**Why it matters:** Snowflake Copilot is the **analyst’s accelerator**. It won’t orchestrate pipelines across systems, but it democratizes data analysis within Snowflake — empowering analysts while freeing engineers from ad-hoc ticket queues.

## Databricks Assistant: A Pair-Programmer for the Lakehouse

**Databricks, on the other hand, approaches the challenge from a developer’s perspective — turning its copilot into a code-focused pair programmer.**

New engineers entering Databricks often face sprawling notebooks filled with PySpark they don’t fully understand. Onboarding and debugging are slow, and code reviews take extra effort.

**Databricks Assistant** changes the dynamic. Highlight code and ask “Explain this” — it breaks down each step in plain English. Paste an error trace, and the Assistant suggests fixes. Need a job scaffolded? “Read CSVs from S3, join with user table, calculate weekly DAUs” produces runnable PySpark.

**Why it matters:** Databricks Assistant isn’t an orchestrator. It’s a developer productivity copilot — reducing debugging time, accelerating coding, and helping teams onboard faster inside the Lakehouse.

## Matillion Copilot (Maia): ETL Jobs in Conversations

**And for teams already building jobs visually, Matillion adds a conversational layer to its familiar ETL canvas.**

Traditional ETL job design means dragging components, configuring them step by step, and hoping it holds together when requirements shift.

**Matillion Copilot (Maia)** makes it conversational. Ask: “Load monthly S3 sales data, join Redshift products, calculate growth, publish to Tableau.” Copilot creates the job visually — connectors, joins, aggregations, outputs — all wired correctly. Want to embed sentiment analysis? It adds the AI step at the right place.

Why it matters: Matillion Copilot is agentic ETL — pipelines explained, validated, and governed. For Matillion users, it’s a leap in speed without compromising control.

## Boomi AI: From Single Copilot to Automation Fleet

**Finally, Boomi broadens the horizon — thinking beyond pipelines and BI, and positioning its AI as an automation fleet for enterprise-scale integration.**

Large enterprises often struggle not with one pipeline, but with integration sprawl — thousands of APIs and processes stitched together.

**Boomi AI** addresses that with a **suite of agents**:

- Connector Agent builds new API connectors.
- Integration Advisor reviews workflows for inefficiency.
- Resolve Agent diagnoses and self-heals failures.
- API Design & Documentation Agents draft specs and docs.

**Why it matters:** Boomi is about **scale and governance**. It’s not just one assistant, but an **AI automation fleet**, best for enterprises aiming for hyperautomation across integration, APIs, and data.

## Comparing the Players

| Vendor/Agent       | Breadth of Scope                                   | Governance Strength                                        | Ideal Users                        |
|--------------------|---------------------------------------------------|-----------------------------------------------------------|------------------------------------|
| Keboola Data Agent | End-to-end: pipelines, debugging, docs, analytics | High – versioning, approvals, audit trails                | Data engineers + analysts         |
| Snowflake Copilot   | Narrow - SQL assistance & analytics                | Very strong - inherits Snowflake RBAC/security           | Analysts, BI users                 |
| Databricks Assistant | Medium – developer-focused code generation & debugging | Strong – Unity Catalog context, workspace security        | Data engineers & scientists       |
| Matillion Copilot (Maia) | Medium – ETL-focused, with conversational job design | Strong – requires plan approval and review                | ETL developers, analytics engineers|
| Boomi AI           | Very broad - integrations, APIs, connectors, automation | Very high - AgentStudio lifecycle management, policies   | Enterprise IT, integration leaders |

## The Future: Openness and Determinism Meet Osiris

All of these copilots are moving fast, but the story doesn’t end here. The next chapter in data engineering isn’t just about speed — the next frontier is trust, reproducibility, and portability.

That’s where **Osiris** comes in — Keboola’s open-source **deterministic compiler for AI-native pipelines**.

### Why it matters:

- Compiler, not just orchestrator → It doesn't only schedule jobs, it generates, validates, and compiles pipelines directly from intent.
- Determinism as a contract → Fingerprints guarantee pipelines are reproducible across environments.
- Conversational → Executable → You describe goals, and Osiris translates them into executable, validated plans.
- Run anywhere, same results → Consistent execution across local and cloud with transparent adapters.
- Boring by design → Predictable, explainable, portable — built for industrial-grade reliability.

## Final Conclusion: Picking the Right Copilot

**So where does this leave us? With a set of powerful copilots, each excelling in different contexts. The real question isn’t which is best on paper — but which works best for your team, with your data, in your stack.**

But here’s the truth: **no online demo or slick launch video can tell you how these copilots will feel inside your workflows**. The only way to know is to **test them side by side**. Spin up a pilot, throw real-world messy data at them, and see which copilot genuinely reduces toil for your engineers and analysts.

## The age of AI copilots is here

The smart move isn’t waiting for perfection — it’s experimenting now, learning what fits, and building the governance practices that let you harness AI safely and at scale.