A Step-by-Step Guide to Mapping Complex Resume Data to Custom Fields in Your ATS Using Make

Transforming raw resume data into structured information within your Applicant Tracking System (ATS) can be a significant challenge, especially when dealing with varied formats and the need to populate custom fields. This guide demystifies the process, providing a comprehensive walkthrough on how to leverage Make (formerly Integromat) to seamlessly parse, filter, and map complex resume data directly into the custom fields of your ATS, ensuring clean, organized, and actionable candidate profiles. By automating this crucial step, you can eliminate manual data entry errors, save valuable time, and enhance the efficiency of your recruitment pipeline.

Step 1: Define Your ATS Custom Fields and Data Points

Before building any automation, a clear understanding of your ATS’s custom fields is paramount. Identify all the specific data points you wish to extract from a resume—such as “Years of Experience,” “Certifications,” “Target Salary,” “Visa Sponsorship Required,” or “Specific Skills (e.g., Python, Salesforce Admin).” Document the exact names of these custom fields in your ATS, as well as their data types (text, number, dropdown, multi-select). This meticulous preparation ensures accurate mapping and prevents data integrity issues down the line. Knowing precisely what information you need to capture and where it resides in your ATS is the foundational step for any successful data automation strategy.

Step 2: Set Up Your Make Scenario with a Webhook Trigger

Initiate your automation workflow in Make by creating a new scenario. The first module in your scenario should be a “Webhook” module, set to “Custom webhook.” This will provide you with a unique URL. Configure your ATS or resume parsing tool (if it supports webhooks) to send the raw resume data, or a parsed JSON/XML output of the resume, to this webhook URL whenever a new resume is processed or uploaded. Alternatively, if your ATS doesn’t support webhooks, you might use a “Watch new records” module with a connector to your ATS, or even an email parser if resumes arrive via email, acting as the initial trigger for your scenario.

Step 3: Parse and Structure Resume Data with Text Parser or AI

Once your webhook receives the resume data, the next critical step is to parse it into a structured format. For less complex data, Make’s “Text parser” module (using regular expressions) can extract specific patterns. However, for genuinely complex and unstructured resume text, an AI-powered module is indispensable. Consider using an “OpenAI” module (or similar NLP service) configured to extract specific entities (e.g., job titles, companies, dates, skills, education) into a structured JSON or object format. Prompt the AI to identify and categorize the precise information you defined in Step 1, ensuring consistency and accuracy in the extracted data points for subsequent mapping.

Step 4: Map Parsed Data to ATS Custom Fields

With your resume data now structured, add an “Update a Record” or “Create a Record” module for your specific ATS connector in Make (e.g., Greenhouse, Workday, Bullhorn). In the configuration for this module, you will see fields corresponding to your ATS’s standard and custom fields. Drag and drop the parsed data elements from the previous parsing module directly into their corresponding ATS custom fields. For instance, if your AI module extracted “Years of Experience,” map that output to the “Total_Experience__c” custom field in your ATS. Pay close attention to data types, using Make’s built-in functions to convert text to numbers or reformat dates if necessary.

Step 5: Implement Data Filtering and Conditional Logic

Not all extracted data will always be relevant or correctly formatted, and you may only want to proceed with certain conditions. Utilize Make’s “Filter” tool (the wrench icon on the connecting line between modules) to add conditions. For example, you might only want to update an ATS field if a specific skill is detected, or if a candidate meets a minimum years of experience threshold. For more complex logic, use a “Router” to create multiple paths based on different conditions, perhaps routing candidates with specific certifications to a separate notification system. This filtering ensures only pertinent and valid data populates your ATS, maintaining data cleanliness.

Step 6: Test, Refine, and Monitor Your Automation

Thorough testing is crucial before deploying your scenario. Run your Make scenario with various sample resumes, including those with different formats and levels of complexity, to observe how the data is parsed and mapped. Check your ATS meticulously after each test run to ensure the custom fields are populated correctly and as expected. Refine your parsing logic (regular expressions or AI prompts) and mapping rules based on the test results. Once satisfied, activate your scenario. Continuously monitor its performance in Make’s “History” tab to identify and troubleshoot any errors, ensuring a smooth and reliable data flow.

If you would like to read more, we recommend this article: The Automated Recruiter’s Edge: Clean Data Workflows with Make Filtering & Mapping