When data engineers need to figure out where to deploy their scripts, they often start with whatever is already at hand. If your source code lives on GitHub, running it with GitHub Actions feels like the natural first step.

It’s right there. You already use it to run tests and deploy applications. The jump from “it runs my CI/CD” to “it runs my production data pipelines” seems harmless enough—especially if your company doesn’t yet have a dedicated data platform.

The appeal is obvious:

• It’s already integrated with your code. No extra setup, no new logins.
  
  

• It comes with free minutes. A GitHub Pro or Team plan gives you 3,000 GitHub Actions minutes per month. If your build and test workflows only use a portion of that, you might have a few hundred minutes left for data jobs.
  
  

• It’s cheap—at first. Under a cent per minute for a standard runner means you can get going without asking for budget approval.
  
  

At the beginning, this works fine. Maybe you run a small data-processing script for 10 minutes a day. That’s just 300 minutes a month—well within the monthly allocation. It’s quick, easy, and costs nothing extra.

But as with many “good enough for now” solutions, trouble starts creeping in once you rely on it for more serious workloads.

Problem #1: GitHub Actions Isn’t Built for Data Engineering

GitHub Actions is a CI/CD tool. It’s excellent at triggering builds, running tests, and deploying applications. But data engineering has very different needs, and Actions simply doesn’t address them.

Once your pipelines go beyond the simplest “run script → exit” model, you’ll notice the missing pieces:

• Metrics and monitoring. CI/CD tools don’t track data volumes, processing durations, or throughput in a way that’s meaningful for data work.
  
  

• Alerting. If a pipeline fails at 2 a.m., you want to be able to know ASAP — not 10 minutes before you’re supposed to be presenting the results the next day
  
  

• Detailed logs. Debugging a failing data transformation often requires richer, longer-lived logs than Actions keeps by default.
  
  

• Orchestration. Complex pipelines need scheduling, dependencies, retries, and branching logic—things that quickly get messy if you try to build them from scratch in YAML.
  
  

• Integrations with data tools. Data engineers need secure, easy connections to secrets managers, data catalogs, warehouses, and lakehouses. CI/CD tools don’t provide these out of the box.
  
  

You can hack some of this together with custom scripts, third-party integrations, and generous amounts of YAML, but it’s duct tape—fine in a pinch, frustrating in the long term.

Problem #2: Costs Spiral as Workloads Grow

What starts as a 10-minute job processing a few thousand rows can easily grow into an hour-long run processing millions of rows. Suddenly, your “free” runner is maxed out.

Let’s do the math. One medium-sized job, running daily:

• Duration: 60 minutes
  
  

• Node size: 2× the standard runner
  
  

• Monthly minutes: 30 days × 60 minutes × 2 = 3,600 minutes
  
  

That’s already over the free 3,000 minutes. And that’s for one job. Add four more like it, and you’re looking at 18,000 minutes a month. 

This cost curve gets worse as you scale. And unlike true data platforms, GitHub Actions doesn’t optimize for throughput or storage locality. You’re paying premium CI/CD prices for infrastructure that’s not optimized for your data engineering workloads.

Problem #3: Reliability Isn’t Production-Grade for Data Pipelines

CI/CD systems like GitHub Actions are designed with the assumption that occasional delays are acceptable. If a build takes an extra five minutes because of platform hiccups, it’s not a crisis.

For production data pipelines, delays can be much more disruptive. You might be delivering analytics dashboards to customers, feeding fresh data into machine learning models, or syncing records to downstream systems on a tight schedule.

GitHub’s own status history shows that Actions does occasionally experience service degradation. For CI/CD, that’s fine. For a production ETL job that needs to land before the 9am dashboard rush, it’s a liability.

Better Options Exist

If you want to run Python-based data pipelines without all these limitations, you have purpose-built alternatives.

One example is Tower.dev. Like GitHub Actions, Tower lets you run any Python script, but it’s designed from the ground up for data engineering. That means:

• Metrics and logs stored in a run database. Every job’s performance and output are tracked for easy debugging and historical analysis.
  
  

• Local execution mode. Test and debug pipelines on your laptop before deploying to production.
  
  

• Control-flow orchestration. Build multi-step workflows with dependencies, retries, branching, and scheduling.
  
  

• Data lakehouse integration. Read from and write to Iceberg tables directly, with built-in catalog awareness.
  
  

In other words, you get the convenience of “just run my script” with the production readiness of a real data platform.

The Bottom Line

GitHub Actions is a fantastic CI/CD platform. But trying to bend it into a production-grade data engineering platform is like trying to use a Swiss Army knife as your main chef’s knife—it technically works, but you’ll quickly reach its limits.

At first, the free minutes and simplicity are seductive. But as workloads grow, costs rise, features are missing, and reliability concerns creep in. If you’re serious about building robust, maintainable data pipelines, it’s worth starting with—or migrating to—a platform designed for that purpose.

Your future self (and your data team) will thank you.