The setting

Copperline Freight moves less-than-truckload shipments across the Midwest, tracking every parcel through a Postgres database behind the dispatch board and the customer portal. You are the database engineer keeping that system fast and correct as volume climbs.

Every mission on this path happens at the same company, so context carries over the way it does in a real job: the data you cleaned in mission two is the data the finance lead questions in mission four.

The missions

1. schema designstarter

Design the shipment-tracking schema for Copperline's new portal

Copperline is building a customer tracking portal, and the whole thing rests on a data model that does not exist yet. Dispatch wrote down what a shipment is and how customers will look it up. You turn that into a schema.

You deliver: A schema design for the tracking portal written as CREATE TABLE statements with keys, constraints, and indexes.

Scored on: Normalized model, Serves the access patterns, Types and constraints, Indexes named.

Working from: tracking_requirements.md.

2. query tuningstarter

Tune the shipment search timing out on the dispatch board

The dispatch board search bar times out at the afternoon peak, and dispatchers are refreshing three times to get a result. You have the query, the plan, and the table shape.

You deliver: A tuning writeup with the diagnosis, a rewritten query, and a way to verify the improvement.

Scored on: Reads the plan, Names the root cause, Correct rewrite, Verification step.

Working from: slow_query.md.

3. indexingcore

Set the indexing strategy for the tracking-events table

The scan_events table is 40 million rows and takes almost a million inserts a day, and someone has been adding an index every time a query felt slow. Now inserts are dragging and some reads are still slow. Time for a real strategy.

You deliver: An indexing plan for scan_events that adds, keeps, and drops indexes with a reason for each.

Scored on: Adds the right indexes, Removes the dead weight, Write-cost aware, Tied to patterns.

Working from: query_patterns.md, existing_indexes.md.

4. migration plancore

Plan the zero-downtime terminal-routing migration on shipments

Product needs each shipment to carry its destination terminal so the portal can filter shipments arriving at a given terminal. A junior wrote a migration that would lock the 2.1 million row shipments table solid, and dispatch runs 24 hours a day with no maintenance window.

You deliver: A zero-downtime migration plan for the destination_terminal_id column with ordered steps, a batched backfill, and a rollback.

Scored on: Avoids long locks, Batched backfill, Rollback path, Correct sequencing.

Working from: change_request.md, table_stats.md.

5. performance reviewstretch

Run Copperline's quarterly database health review

The quarterly database review is due and leadership wants a straight answer: what is wrong, and what should we fix first. Volume is up and the on-call pages are getting more frequent.

You deliver: A quarterly database performance review with a trend read, the top offender, config findings, and ranked fixes.

Scored on: Reads the trend, Finds the top offender, Flags config problems, Ranked fixes.

Working from: db_metrics.csv, slow_query_log.md, db_config.md.

How the scoring works

Each deliverable is graded against the rubric written for that mission. Separately, every mission on every path is graded on how you used AI, against the same four criteria:

  • Understood the task. The learner framed the goal for the assistant clearly instead of pasting the brief and hoping.
  • Grounded in the material. The learner directed the assistant into the provided files and based the work on them, not on invented facts.
  • Verified the output. The learner checked claims, numbers, or coverage against the source material before submitting.
  • Iterated with judgment. The learner refined weak parts of the draft with specific follow-ups rather than accepting the first answer.

Both scores, with the work behind them, go on your proof profile. That is what makes a claim like "I can use AI for database engineering" something an employer can check.