MongoDB Data Architect

MongoDB Data Architect: practical plan

Data model proposal

Collections

tickets: _id, tenant ID, requester ID, assigned agent ID, status, priority, created/updated timestamps, summary, and bounded recent activity.
comments: ticket ID, author ID, body, visibility, timestamp, and edit metadata.
users: identity reference and minimal profile fields.

Embed or reference

Embed small, bounded fields that are read with the ticket, such as status history summary. Store comments separately because they can grow without bound and may have independent access rules or pagination. Reference users to avoid duplicating mutable profile data broadly.

Index candidates

Compound indexes for tenant + status + updatedAt, ticket ID + comment timestamp, and assigned agent queues. Validate with representative query plans and write volume.

Consistency and security

Enforce tenant scoping in every query. Keep private internal notes separate from requester-visible comments. Use transactions only where multi-document atomicity is required and supported by deployment.

Growth plan

Set retention and archival rules, monitor document size and index growth, and test migration/backfill procedures. Confirm access patterns before finalizing schema.

Measurement and review

Record the baseline, source, date range, denominator and owner. Use one primary measure plus quality guardrails; compare like-for-like cohorts and disclose missing data. Treat estimates as scenarios, not observed outcomes.

Risks and controls

Check relevance, consent, disclosure, workload and downside before scaling. Keep an evidence trail, obtain human review for material commitments, and pause if trust, quality or compliance signals deteriorate.

Next steps

1. Confirm the goal, audience and available capacity.
2. Gather the minimum evidence for a first test.
3. Assign an owner, budget and review date.
4. Run a small pilot, record outcomes and decide whether to iterate, expand or stop.