Designing a resume data model sounds simple — until you need it to scale. Here's how Pi AI guided me to build something future-proof and flexible.

I thought I was just building a basic resume feature.

Something like:

  • Name
  • Email
  • Skills
  • Experience

But as the scope expanded — custom layouts, international users, multiple resumes per user — I realized my original schema wouldn't cut it.

I needed something flexible, normalized, and built for growth.

So I turned to Pi AI.

It didn't just spit out boilerplate tables. It asked thoughtful questions. And helped me rethink the structure completely.

🧠 Step 1: Start with Real-World Use Cases

My prompt to Pi:

"How should I structure a database schema for a resume builder app?"

Pi replied:

"Before designing the schema, consider: — Will users create multiple resumes? — Will you support templates and themes? — Should each section (e.g., education, work) be reusable or embedded?"

This made me rethink my assumptions.

✅ Yes, users will want multiple resumes. ✅ Yes, they'll want to reorder or hide sections. ✅ And yes, custom fields might matter for niche industries.

Time to normalize.

📐 Step 2: Modular Table Design with Relationships

With Pi's help, I designed a flexible relational schema.

Here's a simplified version:

🗃️ Core Tables

-- Users table
users (
  id UUID PRIMARY KEY,
  name TEXT,
  email TEXT UNIQUE,
  ...
)
-- Resume (one user can have many)
resumes (
  id UUID PRIMARY KEY,
  user_id UUID REFERENCES users(id),
  title TEXT,
  summary TEXT,
  theme TEXT,
  created_at TIMESTAMP,
  updated_at TIMESTAMP
)
-- Experience section
experiences (
  id UUID PRIMARY KEY,
  resume_id UUID REFERENCES resumes(id),
  company TEXT,
  role TEXT,
  start_date DATE,
  end_date DATE,
  description TEXT,
  order_index INT
)
-- Education section
education (
  id UUID PRIMARY KEY,
  resume_id UUID REFERENCES resumes(id),
  institution TEXT,
  degree TEXT,
  field TEXT,
  start_date DATE,
  end_date DATE,
  description TEXT,
  order_index INT
)

Pi recommended using order_index to preserve custom section order—a key UX detail I hadn't thought about.

🛠️ Step 3: Handling Dynamic Sections and Custom Fields

I asked:

"How should I store dynamic resume sections like awards, certifications, or hobbies?"

Pi suggested:

  • Separate optional tables for common extras (e.g. certifications, projects)
  • A generic custom_sections table with a type, label, and content field for custom user-defined blocks

🧱 Example:

custom_sections (
  id UUID PRIMARY KEY,
  resume_id UUID REFERENCES resumes(id),
  type TEXT, -- e.g. "award", "language"
  label TEXT,
  content JSONB,
  order_index INT
)

The use of JSONB made it flexible while still queryable.

Perfect for storing variable content like:

  • {"award": "Top Developer", "year": "2023"}
  • {"language": "German", "proficiency": "Intermediate"}

🧩 Step 4: Localized and Multi-Format Support

I asked Pi:

"What if users want different versions of the same resume — for jobs in different languages?"

Pi's advice:

  • Store language and region fields in the resumes table
  • Duplicate records for translation, instead of trying to localize sections inline
  • Allow linking resumes to each other with a variant_of field

So I added:

resumes (
  id UUID PRIMARY KEY,
  user_id UUID,
  title TEXT,
  language TEXT,
  region TEXT,
  variant_of UUID -- Self-referencing
)

Now users can have a base resume and variants for German, UK, US job markets.

📊 Step 5: Design Decisions for Performance

Pi also helped me decide:

Feature Design Choice Section reordering order_index integer field Field flexibility Use JSONB for optional sections Text search Add tsvector for full-text Resume duplication Soft-copy via foreign key Multi-resume support One-to-many users → resumes

This kept my schema performant, but still flexible enough for different use cases.

💡 Final Schema Highlights

  • 👥 Multi-resume support per user
  • 🧱 Modular tables per section
  • 🧠 Dynamic sections using custom_sections + JSONB
  • 🌍 Localization via language + variant linking
  • ✍️ Order preservation for resume UI rendering

Thanks to Pi's prompts, I felt like I was designing with a product architect, not just an AI assistant.

🙌 Takeaways

  • Pi doesn't just give answers — it asks you the right questions
  • It's perfect for early-stage design thinking
  • I saved hours of trial-and-error by talking through my ideas with Pi

If you're building a product that involves structured content like resumes, profiles, or form builders — Pi can guide you like a smart co-architect.

💬 What About You?

🧠 Ever used AI to design a database? 📎 Share your schema questions or use cases in the comments 👏 Clap if this saved you some design time 🔗 Share this with your team or fellow devs building resume builders, HR tools, or personal sites

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