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Data Migration Workflows

Summary

Planned and executed structured data migrations between systems or formats, with an emphasis on validation, auditability, and repeatable process design that could be run safely more than once.

Context

Data migrations in practice are rarely one-time events. Requirements change, edge cases appear after the first pass, and the ability to re-run a migration cleanly — or to audit what happened — turns out to matter more than it seems upfront.

Problem

Moving data between systems or formats while maintaining integrity, handling edge cases without silent failures, producing an audit trail, and leaving the process in a state where it can be repeated or adjusted if requirements shift.

My role

I designed the migration workflow, wrote the tooling to extract, transform, and load the data, built in validation at each stage, and documented the process well enough that it could be re-run or handed off.

Approach

  • Mapped source to destination fields, documenting assumptions and transformation rules
  • Built the migration in stages: extract, validate, transform, load, verify
  • Added logging at each stage to capture what was processed and what was skipped or flagged
  • Ran the migration against a non-production environment first and compared results
  • Addressed edge cases iteratively rather than trying to handle everything upfront
  • Produced a migration report summarizing counts, errors, and any manual review items

Technical considerations

  • Field mapping and normalization across different data models
  • Handling nulls, unexpected values, and encoding inconsistencies
  • Idempotency — ensuring the migration could be re-run without duplicating data
  • Row-level error capture rather than failing the entire run on a single bad record
  • Auditability of what was migrated, skipped, or flagged for review
  • Validation of destination data against source before sign-off

Outcome

The migration completed with a validated record count, a documented exception list, and a process that could be re-run cleanly if needed. Post-migration audits confirmed data integrity in the destination system.

Skills demonstrated

  • Data modeling and field mapping
  • ETL workflow design
  • Validation and error handling
  • Auditability and logging
  • Repeatable process design
  • Technical documentation