Engineering task library · Version 5 Work that feels like work. Design a system. Diagnose tail latency. Ship a migration. Recover a failed rollout. Pick a focused ticket or follow a project through its delivery phases.
1380 tickets · 138 projects · 26 engineering fields · Four difficulty levels
Practice briefs Fictional engineering practice briefs. Starter repositories, fixtures, automated grading, and verified ownership are not included. Completing a ticket does not issue a credential.
AI tools are welcome during implementation. Record assumptions, review the result, and verify its behavior.
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.
Field counts and the engineering field filter use each project's primary field. Estimated ticket labels can span multiple fields.
Backend 60 Data engineering 50 Platform engineering 50 Security 50 Frontend 60 Mobile 50 Developer tooling 50 Quality engineering 50 Applied AI 50 Integrations 60 Real-time systems 50 Accessibility 50 System design 60 Performance engineering 50 Database engineering 50 Distributed systems 60 Site reliability 50 Cloud infrastructure 50 Networking 50 API design 50 Storage systems 50 Compiler and language tooling 60 Embedded and edge 50 Privacy engineering 50 Systems programming 60 DevOps 60 PWEB-105 Story Advanced
Every filter mounts hundreds of offscreen job rows. A first virtualization attempt is faster but drops focus when the selected row scrolls away.
Estimated field mix
Performance engineering 40% Frontend 30% Accessibility 30% Make a large scheduling screen respond to the next clickPWEB-106 Bug Advanced
Date and duration formatting dominates repeated filters. A broad memoization patch reuses labels after the schedule time zone changes.
Estimated field mix
Performance engineering 60% Frontend 40% Make a large scheduling screen respond to the next clickPWEB-107 Story Expert
Fast typing schedules several expensive filter computations. Results from an earlier query briefly replace the latest selection and consume time after they are irrelevant.
Estimated field mix
Performance engineering 50% Frontend 50% Make a large scheduling screen respond to the next clickPWEB-108 Bug Advanced
The screen starts responsive and degrades after operators open and close it throughout a shift. Detached rows remain reachable through subscriptions.
Estimated field mix
Performance engineering 60% Frontend 40% Make a large scheduling screen respond to the next clickPWEB-109 Chore Intermediate
The fastest desktop run became the benchmark. It misses the narrow layout and empty-filter transition used by coordinators on older laptops.
Estimated field mix
Quality engineering 50% Performance engineering 30% Frontend 20% Make a large scheduling screen respond to the next clickPWEB-110 Task Expert
Several changes reduced different counters. The team needs a release decision tied to the actual filter-and-select workflow and its accessibility contract.
Estimated field mix
Performance engineering 50% Quality engineering 30% Accessibility 20% Make a large scheduling screen respond to the next clickPBATCH-101 Task Foundational
The demonstration file has one short record per line. It cannot expose parsers that split quoted descriptions or allocate one enormous record.
Estimated field mix
Data engineering 50% Quality engineering 30% Performance engineering 20% Import a large supplier catalog without exhausting the workerPBATCH-102 Task Foundational
The worker exits near the end of an import, but the existing memory sample is taken only after garbage collection and misses the peak.
Estimated field mix
Performance engineering 70% Data engineering 30% Import a large supplier catalog without exhausting the workerPBATCH-103 Story Intermediate
A proposal to increase database concurrency assumes writes dominate, but expensive normalization may already saturate one CPU core.
Estimated field mix
Performance engineering 60% Data engineering 40% Import a large supplier catalog without exhausting the workerPBATCH-104 Story Advanced
Switching to a streaming file reader did not reduce memory because validation still accumulates every parsed row before writing starts.
Estimated field mix
Performance engineering 50% Data engineering 50% Import a large supplier catalog without exhausting the workerPBATCH-105 Story Advanced
Single-row inserts dominate after streaming is introduced. A bulk-insert experiment is faster but resolves duplicate supplier SKUs differently.
Estimated field mix
Database engineering 50% Data engineering 30% Performance engineering 20% Import a large supplier catalog without exhausting the workerPBATCH-106 Bug Expert
Parallel validation improves throughput until one slow record delays output. Later completed records accumulate while the writer waits for order.
Estimated field mix
Performance engineering 50% Data engineering 50% Import a large supplier catalog without exhausting the workerPLATENCY / Performance engineering Explain and reduce quote latency under a mixed workload without weakening pricing correctness.
PQUERY / Performance engineering Tune a tenant-scoped PostgreSQL inbox using plans, representative distributions and reversible changes.
PWEB / Performance engineering Investigate browser loading, rendering and interaction costs while preserving usable scheduling workflows.
PBATCH / Performance engineering Make a bounded catalog import stream, recover and report progress under explicit resource limits.
PCACHE / Performance engineering Measure cache value, prevent refill storms and bound staleness without hiding origin failures.
Take the brief into your own workflow. For engineers Practice scoped changes, keep a portable implementation and verification record, and learn to explain operational tradeoffs. Choose a ticket whose prerequisites you can provide.
For companies Use realistic work to structure onboarding, internal practice, and conversations about engineering decisions. Each project names the delivery benefit. Agree scope and compensation before requesting company-specific work.
CSV contains one row per ticket. Map fields and issue types in your tracker; project grouping and dependency keys are descriptive. JSON preserves the complete project structure. These downloads do not synchronize with Jira.