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Structure for the Long Game

Overview

During my 2026 job search, I needed a way to manage opportunities, networking, company research, resume versions, and unemployment reporting. Existing tools were either too rigid or too shallow. I designed a governed, taxonomy-driven Career CRM that reflects how I approach any complex information problem: structured, scalable, and grounded in metadata clarity.

This system now serves as my long‑term career infrastructure — a personal CRM built with the same rigor I apply to enterprise taxonomy and metadata projects.

Context

Job searching generates a surprising amount of unstructured information: job postings, referrals, conversations, research, resume versions, and deadlines.

I needed a system that could handle:

  • multiple roles and industries
  • networking paths
  • strategic company research
  • resume and materials management
  • unemployment reporting requirements

Rather than rely on fragmented tools, I built a unified CRM in Excel using information‑architecture principles.

Problem

Traditional job‑tracking tools treat everything as a flat list. But job searching is not flat — it’s relational. I needed a system that could:

  • distinguish people from companies
  • separate strategic companies from one‑off applications
  • track research and warm paths
  • manage resume versions
  • maintain clean metadata over time

The challenge was designing a system that was flexible enough for real‑world variability but governed enough to stay clean for years.

Approach

I approached the project as a taxonomy and metadata design problem.

The goals were to:

  • define clear entities
  • establish semantic blocks
  • create controlled vocabularies
  • write scope notes for field‑level clarity
  • write governance rules for system‑wide consistency
  • normalize company names
  • design lightweight relational logic
  • build a materials library for resumes and one‑pagers

This ensured the system would scale without becoming chaotic.

Process

Entity Modeling

I identified four core entities:

  • Opportunities — every job I apply to or consider
  • TargetCompanies — companies worth deeper research
  • NetworkingContacts — authoritative identity store for people
  • Materials — resumes, one‑pagers, cover letters, and portfolio assets

This mirrors enterprise CRM architecture.

Semantic Blocks

Grouping related fields into conceptual zones reduces cognitive load and makes scanning more efficient. Thus each sheet was structured into conceptual blocks such as:

  • Identity
  • Relationship
  • Strategic fit
  • Research
  • Workflow
  • File management

This reduces cognitive load and makes scanning easier.

Controlled Vocabularies

I created governed vocabularies for:

  • RelationshipType
  • Warmth
  • StrategicCategory
  • ApplicationStatus
  • Material Type
  • Research Status

These taxonomies keep the system consistent and filterable.

Scope Notes

I wrote Scope Notes for fields that needed boundaries, including:

  • Primary Contact
  • Fit
  • Material Name
  • Referral fields
  • Company Name normalization

These prevent drift and ensure consistent interpretation.

Governance Rules

I documented system‑wide rules for:

  • naming conventions
  • normalization
  • workflow timing
  • promotion rules (when a company becomes strategic)
  • identity vs. reference distinctions

This governance layer keeps the system coherent over time.

Relational Logic

I used Opportunity Number as the universal connector across sheets, with Company Name as a secondary connector. This provides relational structure without requiring  database infrastructure or technical overhead.

Materials Library

I added a dedicated sheet to track:

  • master resume templates
  • customized resumes
  • one‑pagers
  • cover letters
  • portfolio assets

Each asset includes metadata such as type, version, file location, and associated opportunity.

Key Decisions

  • Not every company belongs in TargetCompanies. Only strategic companies are added.
  • Primary Contact is a reference, not an identity record. NetworkingContacts is authoritative.
  • Company Name normalization prevents dirty data. No legal suffixes, no retroactive renaming.
  • Opportunity Number is the relational backbone. Used across Opportunities, Materials, and reporting.
  • Governed vocabularies + free text where appropriate. A classic metadata governance pattern.

These decisions keep the system clean, scalable, and easy to maintain.

Outcome

The result is a governed, taxonomy‑driven Career CRM that:

  • supports my job search with clarity and structure
  • reduces cognitive load and decision fatigue
  • maintains clean metadata over time
  • tracks resume versions and materials
  • supports unemployment reporting
  • provides a research framework for strategic companies
  • reflects how I think as a taxonomist and information architect

This system will scale with me for the next decade.

What I Learned

  • Taxonomy thinking applies beautifully to personal systems.
  • Governance prevents chaos before it starts.
  • Scope Notes are essential for long‑term clarity.
  • Metadata decisions shape workflow and behavior.
  • Designing your own tools is a form of self‑advocacy.

Conclusion

This project began as a way to organize my job search and evolved into a governed information system. It reflects my hybrid identity as a taxonomist, metadata architect, and UX‑minded systems thinker. The Career CRM now serves as both a practical tool and a demonstration of how I approach complex information problems.