Suggested answer

I run it as a repeatable pipeline that gets rehearsed, not as a single event:

1. Profile and scope: Understand volumes per object, relationship depth, data quality, and what genuinely needs to migrate. A significant share of legacy data usually should not come across at all, and agreeing that early removes the most risk.
2. Design the keys: Every object gets an External Id holding the legacy identifier, marked Unique. This makes loads idempotent via upsert and lets children reference parents by legacy key, which removes the entire Id-mapping pass.
3. Cleanse in staging: Deduplicate, standardise formats, and validate picklist and reference values against the target org before anything is loaded. Fixing data in production afterwards costs several times more.
4. Sequence the load: Users and reference data, then parents, then children, then junctions, then attachments and files. Load relationships by External Id rather than resolved Ids.
5. Tune for volume: Bulk API 2.0, non-essential automation suspended for the window, sharing calculation deferred, batches sorted by parent Id, parallel where there is no parent contention and serial where there is.
6. Handle audit fields: If CreatedDate and ownership must be preserved, enable Set Audit Fields upon Record Creation first — those fields can only be set at insert, so missing it means deleting and reloading.
7. Rehearse and reconcile: Full dress rehearsal in a sandbox with production-like volume, then reconcile with row counts, control totals on key amounts, referential integrity checks, and business sign-off on a sample. Then re-enable automation and recalculate sharing once.
8. Plan the cutover: Delta load for records that changed during the rehearsal-to-cutover window, plus a documented rollback position.

Practice content for interview preparation; not an official vendor answer. Verify details against current product documentation.

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