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Controlled Vocabularies

By dan

Human dan

I'm interviewing for a senior engineer position and would like to do some light interview prep. The team I'd be joining is migrating and modernizing a legacy application. That application is used by organizations around the world, so it is key that the migration is smooth and painless.

Responsibilities

  • Support legacy data migration efforts
  • Prepare and validate customer data for migration
  • Execute and monitor data load activities
  • Investigate and resolve migration related activities
  • Implement fixes and enhancements to migration tooling and related application code
  • Deliver minor user interface improvements to support migration workflows.
  • Conduct code reviews and ensure code quality standards
  • Solve complex technical problems and identify practical solutions
  • Collaborate with stakeholders on migration priorities and outcomes

Qualifications

  • 4+ years of software dev experience
  • Python (Django): Strong experience building and maintaining server-side applications. Comfortable with Django management commands, ORM bulk operations, transactions, and service-layer patterns. Experience with the data migration domain (ETL, data mapping, checkpointed jobs) is a plus.
  • Data migration and ETL: Hands-on experience moving structured data between systems. Familiarity with XML/CSV parsing, metadata schemas, controlled vocabularies Controlled Vocabularies You are here
This branch begins here Controlled Vocabularies
Human dan

What are examples of "controlled vocabularies"?

AI gpt-5.6-terra

Controlled vocabularies are predefined, governed sets of allowed values used to keep data consistent across records, systems, and organizations. Rather than allowing free-text input like "urgent", "Urgent", "HIGH PRIORITY", or "ASAP", a system requires one approved value such as high.

Common examples:

  • Status values
  • draft, active, inactive, archived

  • pending, approved, rejected

  • Priority levels

  • low, medium, high, critical

  • Country and region codes

  • ISO 3166 country codes: US, GB, DE, JP

  • Subdivision/state codes such as US-CA

  • Languages

  • ISO language codes: en, fr, es, de

  • Locale codes: en-US, en-GB, fr-CA

  • Currency codes

  • ISO 4217: USD, EUR, JPY, GBP

  • Date and format identifiers

  • A defined set such as ISO-8601, MM/DD/YYYY, DD/MM/YYYY

  • In practice, migrations should generally normalize dates into one canonical format rather than retain a format label.

  • Product or account types

  • individual, business, nonprofit, government

  • standard, premium, enterprise

  • Document or record classifications

  • invoice, purchase_order, contract, receipt

  • public, internal, confidential, restricted

  • Industry-standard domain vocabularies

  • Healthcare: ICD-10 diagnosis codes, SNOMED CT concepts, LOINC lab-test codes
  • Libraries/archives: Library of Congress Subject Headings, Dublin Core metadata terms
  • Geography: GeoNames identifiers

  • Education: standardized subject or grade-level codes

  • Internal organizational vocabularies

  • Department codes: ENG, FIN, HR, SALES
  • Migration source-system labels: legacy_crm, erp_v2, manual_import
  • Data-quality disposition values: valid, needs_review, invalid, unmapped

In a migration, controlled vocabularies matter because legacy systems often contain inconsistent free-text values. A migration pipeline may map variants into canonical target values:

PRIORITY_MAP = {
    "urgent": "critical",
    "asap": "critical",
    "high priority": "high",
    "med": "medium",
    "normal": "medium",
    "low": "low",
}

A senior-level answer would also mention what happens when a value is not recognized: do not silently guess. Record the original value, flag or quarantine the row, report it clearly, and use an agreed mapping or stakeholder decision before loading it into the target system.

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