Diagnose and Rebuild a Slow Recipe Search at a Marketplace Startup
Overview
What this challenge is about.
Diagnose and Rebuild a Slow Recipe Search at a Marketplace Startup. Beginner-friendly challenge in code. Writing production code that solves real engineering...
The Brief
What you'll do, and what you'll demonstrate.
Determine why the recipe search runs roughly 22 times slower than it did two years ago and ship a redesign that returns results in under 50 milliseconds on the current catalog.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Analyze the time complexity of an existing function and tie it to observed latency growth
- Compare index-based and hash/trie-based search structures against a real query mix
- Implement a correctness-preserving optimization that materially reduces latency
- Design and run a reproducible benchmark that proves a performance claim
- Communicate an engineering trade-off decision concisely to a small team
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Algorithm Analysis
Apply algorithm analysis to solve real industry problems and demonstrate production-level capability.
- Big O
Apply big o to solve real industry problems and demonstrate production-level capability.
- Data Structures
Apply data structures to solve real industry problems and demonstrate production-level capability.
- Hash Maps
Apply hash maps to solve real industry problems and demonstrate production-level capability.
- Benchmarking
Apply benchmarking to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Backend Engineer
Backend engineers routinely inherit code that worked at small scale and broke at large scale. This challenge rehearses the exact loop of diagnosing a hot path, choosing the right data structure, and proving the fix held up under measurement before it ships.
This challenge sharpens
- algorithm-analysis
- data-structures
- python
Performance Engineer
Performance work lives or dies on credible measurement. Here you build a reproducible benchmark, report percentile latency rather than averages, and defend a complexity argument with numbers, which mirrors how performance engineers justify optimizations to a team.
This challenge sharpens
- benchmarking
- big-o
- python
Search Engineer
Building an inverted index keyed on titles and ingredients is the foundational move behind real search systems. This challenge introduces the core indexing and hash-lookup patterns that scale search engineering work, on a small enough dataset to reason about end to end.
This challenge sharpens
- data-structures
- hash-maps
- algorithm-analysis