Mastering Custom Code Automation with n8n: Transforming Job Listings Programmatically

Mar 5 / Vadim Vozmitsel
Ever feel like you're drowning in a sea of messy APIs and disconnected AI flows? Trust me, I've been there. Those endless JSON files that look more like alien code than useful information—yeah, we're about to fix that.

Today, we're going to show you how to turn those data headaches into crystal-clear insights using nothing more than JavaScript and n8n. Think of it like being a data translator, transforming cryptic job listings into something your grandma could understand.

Become a versatile AI Engineer 

When Data Fights Back

Let's say you are starting to scope or are already working on a multi-provider API aggregation project. In the context of this article, we are looking at wrangling large amounts of job listings from RapidAPI APIs like JSearch, and the initial raw data that comes out of these APIs looks like someone threw a dictionary into a blender. Employer names, job titles, salaries—all buried so deep you'd need a mining expedition to find them.

Our mission? Cut through the noise and make this data actually useful.

Step aside AI: Javascript to the rescue 

n8n's custom code node is about to become your new best friend. We're going to use JavaScript like a surgical instrument, slicing and dicing data with precision.

Custom Code script we use in n8n Code node

 Let's unpack what's happening in this script:

  • Input Handling: $input.all() retrieves all input items, ensuring no data is left behind.
  • Relying on defensive programming, we enforce multiple null checks prevent script crashes
  • || 'N/A' provides fallback values
  • Then we employ map() over and flat() out methods handle array transformations

Template Literals: Create human-readable sentences dynamically

Using custom scripting allows us to save time and token costs dramatically. It is a myth that AI should handle everything, instead, it is our job as digital engineers to determine what
  • Using template literals we can interpolate job details seamlessly
  • While handling optional fields gracefully

Using this script, we can turn incredibly long responses into semantically structured and well organized data.

Original unprocessed API response from RapidAPI JSearch API

The 77,320 character response above is transformed into a 33,827 human readable object that can then be easily further processed downstream by AI or a human or both

Why this approach rocks

  • Cost-Effective: No expensive AI token usage
  • Performant: Lightning-fast JavaScript processing
  • Flexible: Easily adaptable to different data structures
  • Readable: Transform complex data into clear narratives

AI & code work well together

While our JavaScript does the heavy lifting, we can further enhance our workflow by integrating OpenAI for additional processing. Imagine generating tailored application strategies or summarizing job descriptions—all within the same n8n workflow.

Practical applications for Dual Code-AI Processing

  • Job Market Analysis
  • Automated Job Matching
  • Recruitment Pipeline optimization
  • Personal Job Search Assistants

The future of AI Administered Automation

This isn't just code or simple LLM interaction—it's a blueprint for intelligent data handling. By combining programmatic techniques with strategic integrations, we're not just processing data; we're creating meaningful, actionable insights.

As Artificial Intelligence continues to gain more and more performant architectures, the throughput and quality of the processing that it provides will also get much better.

However, it is unlikely that we will be able to fully "do away" or abstract code. For end users and non-technical mainstream products, maybe. But if you are looking to become a valuable and capable AI Engineer, it is wise to learn these types of hybrid code-AI methods so that you never run into hard blocks in your development process.

In isolation, code and AI will continue to face blind spots inside complex, real world environments. But when combined inside hybrid solutions where they both get to excel in areas they are designed for, your engineering and development limitations become almost non-existent.

Best part is, most modern flagship frontier LLMs like Anthropic Sonnet 3.7, OpenAI latest o3 and GPT plus all the highly intelligent choices from DeepSeek, Qwen and many other providers can now comfortable write all of the code for you. The only thing they require is comprehensive prompting and very detailed and specific directives. 

So yes, you can forego learning Javascript or Python in an expert way, but no, you can not completely avoid dealing with code no matter how code/no-code/low-code your stack is. 

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Automation isn't about replacing human judgment—it's about empowering it. Each line of code we write is a step towards more intelligent, efficient workflows.

Ready to transform your data? The journey starts here.
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