Scenario Based DE problems. Run and test in browser.
For candidates who've been ghosted on data-engineering applications and can't pinpoint what's missing — the SQL, the take-home, the level you're targeting. Pick a pattern and climb.
The querying spine — 18 patterns, each a map of the exact traps, with the one-line truth and a runnable counterexample.
Open-ended Kafka / Spark / dbt prompts with senior-reviewer feedback. Bundled with the coached tier.
SQL patterns
10 patternsData Quality & Reconciliation
Python patterns
21 patternsAggregation & grouping
Data-shape transforms
DataFrame selection
Scheduling & backfill
Graph & dependencies
Retry & idempotency
Joins & merges
Missing data
Parsing
Sorting & ranking
Vectorization
String cleaning
Deduplication
Datetime handling
Semi-structured
Schema validation
Iteration & filtering
Windowing & sessionization
Streaming & state
Set operations
Dtype management
Most data engineering practice platforms miss what hiring teams actually evaluate. They either repackage LeetCode-style puzzles as SQL problems or hide shallow learning behind a paywall. Our problems focus on real screening gaps: choosing LEFT JOINs over INNER JOINs, using the right window frame, handling nulls correctly, and writing CTEs that hold up under messy data. Once you have solved enough to understand your weak spots, the assessment identifies them clearly and gives targeted projects to help you close those gaps.
When you've done enough problems to know where your gaps are, the assessment names them precisely and gives you the projects to close them.