Realistic take-home practice for data candidates

The idea

Data candidates prep algorithms on LeetCode and SQL syntax on DataLemur, then hit the actual filter — a 48-hour take-home with a messy warehouse and an ambiguous prompt — having never once rehearsed the format. The take-home tests things nobody practices: triaging unfamiliar schemas under time pressure, spotting planted data traps, stating assumptions, and writing the summary memo that hiring managers actually read. Candidates bomb it and never learn why, because rejections come without feedback.

DataExpert proved this audience pays real money for career-advancing education. But courses teach concepts; this rehearses the evaluation. The wedge is fidelity — datasets with fan-out joins, timezone traps, and soft deletes, graded by executing your SQL against reference results plus an AI rubric on the memo. DuckDB-WASM makes the economics absurd: the entire warehouse runs in the candidate’s browser tab, so marginal infra cost per user is a Parquet file download. Nobody owns take-home practice the way LeetCode owns algorithms, and the category is sitting there.

Who pays

Job-hunting analysts and analytics engineers, the same wallet that funds LeetCode Premium during a search — urgent, short-lived, willing. $24/mo or $79 for six months, two free challenges as the funnel. Distribution: r/dataengineering and r/analytics, data-career YouTube and LinkedIn creators (affiliate deals), and SEO on take-home-specific queries that currently return nothing useful.

MVP scope

• 10 hand-built challenge datasets across analyst and analytics-engineer roles, served as Parquet into DuckDB-WASM
• Timed workspace: schema browser, SQL editor, results pane, summary memo panel
• Deterministic correctness diffing plus Claude rubric grading with model solutions
• Progress dashboard and challenge history
• Stripe subscriptions with two free challenges
• Skip for v1: dbt project challenges (fake it with SQL models), Python notebooks, mock-interview video mode, employer-side product

Build QueryRep, a take-home rehearsal gym for data analysts and analytics engineers. Stack: Next.js, DuckDB-WASM running entirely in the browser, Claude API for grading, Stripe for subscriptions.

Core flow: user picks a challenge from a library of simulated take-homes — each one is a messy multi-table warehouse (8-15 tables, nulls, duplicate keys, mixed grain, a deliberately ambiguous prompt like the real thing) loaded into DuckDB-WASM from hosted Parquet files. A timer starts (90 or 120 minutes). The workspace has a schema browser, SQL editor with run-query results pane, and a written-summary panel, because real take-homes grade the memo as much as the SQL. On submit, Claude grades against a hidden rubric: correctness checked by running the candidate query against reference results, then dimension scores for handling the data traps, query readability, assumption-stating, and the written summary. The user gets a rubric report with specific line references and a model solution walkthrough.

Non-obvious details: grade correctness deterministically by executing both queries in DuckDB and diffing result sets — never let the LLM judge numbers. Seed each dataset with 3-4 named traps (timezone-shifted timestamps, soft-deleted rows, fan-out joins) and have the rubric explicitly check whether each trap was handled.

Pages: challenge library with difficulty and role filters, timed workspace, report page, progress dashboard, pricing.

Pricing: $24/mo or $79 for 6 months; 2 free challenges.