Task library LAKE Data engineering · Phased project Rebuild a trustworthy merchant analytics mart A fictional marketplace reports daily sales to merchants. Refund joins inflate revenue, merchants close books in different time zones, and backfills compete with nightly loads. All source orders and merchants are synthetic.
Practice brief · Version 5
Project scope 10 tickets / 3 phases
Total focused work estimate 27h + setup
Suggested stack SQL · PostgreSQL · TypeScript · Object storage Fictional engineering practice briefs. Starter repositories, fixtures, automated grading, and verified ownership are not included.
Choose a bounded subset for an assessment or practice session. Prerequisites and remaining project work must be agreed separately.
Field percentages are editorial estimates of the ticket's engineering focus. They total 100%; they are not measured time, proficiency scores, or ownership evidence.
Pattern topics identify design choices to practice. Read the ticket's acceptance criteria and justify the simplest suitable approach. Tags are not capability or ownership evidence; an untagged ticket has no curated pattern topic assigned.
Recommended next step Start with LAKE-101 Open the first ticket for its prerequisites, acceptance criteria, verification plan, and an editable task draft.
What the engineer takes away Practice metric contracts, historical dimensions, incremental processing, and reproducible warehouse releases.
What the team gains Inspect whether an engineer can reconcile business totals and explain metric changes in a reviewable data product.
Before you start SQL joins and aggregates Batch pipelines Metric definitions Delivery agreement Ten issues across definition, modeling, and release; deliver SQL, synthetic reconciliation fixtures, and an analyst handoff.
AI tools are welcome during implementation. Record assumptions, review the result, and verify its behavior.
Delivery phases PHASE 1 Make metric grain and source assumptions explicit.
PHASE 2 Handle refunds, dimensions, dates, and incremental updates.
PHASE 3 Reconcile, backfill, and govern a versioned dataset.
Agree on the numbers Make metric grain and source assumptions explicit.
LAKE-101 Entry ticket; project setup still required LAKE-102 Entry ticket; project setup still required Estimated field mix
Data engineering 70% Database engineering 30% Build reliable models Handle refunds, dimensions, dates, and incremental updates.
LAKE-103 Depends on LAKE-101, LAKE-102 Estimated field mix
Data engineering 80% Database engineering 20% LAKE-104 Depends on LAKE-101 LAKE-105 Depends on LAKE-102, LAKE-104 Estimated field mix
Data engineering 70% Database engineering 30% LAKE-106 Depends on LAKE-103, LAKE-104 Estimated field mix
Data engineering 70% Database engineering 30% Release the mart safely Reconcile, backfill, and govern a versioned dataset.
LAKE-107 Depends on LAKE-105, LAKE-106 Estimated field mix
Data engineering 60% Distributed systems 40% LAKE-108 Depends on LAKE-103, LAKE-106 Estimated field mix
Data engineering 60% Quality engineering 40% LAKE-109 Depends on LAKE-108 Estimated field mix
Security 50% Data engineering 30% Privacy engineering 20% LAKE-110 Depends on LAKE-107, LAKE-108 Estimated field mix
Data engineering 80% Site reliability 20% Use this project CSV keeps grouping and dependency keys as descriptive fields. Import mapping depends on your tracker configuration.