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Practice real SQL queries, Python transformations, Polars pipelines, and quant finance problems in a browser IDE — with instant automated feedback on every submission.
No credit card required · 25 free problems
import polars as pl def solve(raw_customers: pl.DataFrame) -> pl.DataFrame: # Deduplicate keeping newest record per customer, # then assign surrogate keys 1..N return ( raw_customers .sort(["customer_id", "updated_at"], descending=[False, True]) .unique(subset=["customer_id"], keep="first", maintain_order=True) .sort("customer_id") .with_row_index("customer_key", offset=1) .select(["customer_key", "customer_id", "name", "email", "segment"]) )
Everything you need to interview confidently
Not another set of toy puzzles. Real tools, real data, real feedback.
Real execution sandbox
Your code runs inside an isolated Docker container against actual datasets — pandas DataFrames, Polars pipelines, and DuckDB for SQL. No toy evaluators.
Instant per-test feedback
See pass/fail on every test case in seconds, including diffs between expected and actual output for visible cases. Hidden cases reveal only a count.
Dataset previews
Every problem ships with real seed data. Preview schema and sample rows before you write a single line, and Run to see raw output instantly.
Progress dashboard
Track solved problems by difficulty and topic, keep your daily streak alive, and review your full submission history.
Quant finance track
Black-Scholes pricing, implied volatility solvers, EWMA volatility estimation, Sharpe ratios — problems you'll actually see at quant trading desks.
Structured curriculum
Problems tagged by concept — window functions, SCD2, star schema, ETL pipelines, time-series — so you can work through gaps systematically.
Three tracks, one platform
Cover every question type you'll encounter in data engineering and quant finance interviews.
SQL & Analytics
- Monthly Active Users with window functions
- Revenue cohort analysis
- Sessionisation from event logs
- Rolling 7-day retention
Python & pandas
- Daily log-returns from price series
- Sharpe ratio computation
- Black-Scholes option pricing
- Implied volatility via Newton's method
Polars pipelines
- Flatten JSON event logs
- SCD Type 2 customer history
- Customer dimension deduplication
- EWMA volatility over partitions
The Polars SCD2 question was exactly what came up in my DE interview at a major fintech. Passed first round.
Data engineer, Series B startup
Finally a platform that runs real code instead of checking string output. The sandbox is legit.
Quant developer, prop trading firm
Black-Scholes and implied vol problems in one place — my quant prep went from weeks to days.
Junior quant, investment bank
Simple, honest pricing
Start free. Upgrade when you need the full library. Cancel any time.
Free
Enough to get a feel for every track and start building momentum.
- 25 problems (SQL, Python, Polars)
- Unlimited submissions
- Run & preview mode
- Progress dashboard
- Community access
- Hidden test cases
- Quant finance track
- Advanced data modelling
Pro
Full access to every problem, track, and hidden test case.
- Everything in Free
- Unlimited problems (100+)
- Hidden test cases revealed on pass
- Quant finance track (20 problems)
- Advanced data modelling track
- Priority submission queue
- Download your solutions
Team
For hiring teams running technical screens or cohorts of engineers upskilling together.
- Everything in Pro
- Team dashboard & analytics
- Custom problem sets
- Candidate invite links
- Submission audit log
- SSO / SAML
- Dedicated support
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