Ramp is corporate spend analytics. Practice month-over-month growth, top-N vendor rankings, running totals and pivot reports on a synthetic card-transaction ledger and the app’s ecommerce tables, with an AI tutor when you get stuck.
32 challenges
Ramp-pattern set
Runnable data
Orders + a card ledger
AI tutor
Step-by-step hints
What this page is: we have no dated public source for how Ramp runs its SQL round, so this page does not state one — no duration, no platform, no stage list. What follows is general SQL interview practice on the kind of data Ramp works with. What is ours: the questions are SQL Quest challenges and the topic emphasis is our editorial judgement, not a measured breakdown of Ramp's interview. Pages with dated sources say so in this spot.
The 32 challenges tagged Ramp in the SQL Quest bank, with every raw challenge tag resolved to the 9 canonical skills. Each share is the portion of those 32 challenges that exercise the skill — a challenge exercises several, so the shares do not sum to 100%. This is the composition of the practice set on this page, not a measurement of Ramp’s interview.
These are SQL Quest challenges chosen because their SQL matches the work — Querying Basics, Aggregation & Grouping, Subqueries & CTEs. They are not questions Ramp has asked, and this page does not claim to know its questions. All of the six play free; each card opens the challenge itself.
General guidance about analytics SQL work — not sourced from Ramp and not a description of its process. We have no dated, citable source for how Ramp runs its SQL round, so this page states none.
Skillmap
Ten questions, no signup. You get a readiness score weighted to the SQL this page covers, your Skillmap across joins, window functions, aggregation and the rest, and the weakest skill to practise first.
Drill the skills the Ramp set leans on, one at a time: GROUP BY exercises · CTE practice · Window function practice · CASE WHEN practice · Date function practice — or browse every SQL practice question.
Every question in the Ramp set, one page each with the schema and a hint: Monthly Order Count · Top 5 Earners · January 2024 Orders · Monthly Spend Per Account · Spend by Day of Week · Pivot: Order Status by Country · Below Department Average · Quarterly Hiring Cohort Report · Performance vs Salary Analysis · Monthly Order Trends · Conditional Counting with CASE · Monthly Order Volume in 2024 · Inactive Customers by Tier · Month-over-Month Customer Growth · Customer Signup Quarter (Date + CASE) · Active Spender Cohort (HAVING) · RANK vs DENSE_RANK Side-by-Side · Top 3 Merchants Per Category by Spend · Ticket-Size Mix by Category · Month-over-Month Spend Growth by Category · Cumulative Distinct Customers Over Time · Running Total Revenue · Multi-CTE Revenue Pipeline · Cumulative Revenue Share (Pareto) · Top Spender Per Country · Nth Highest Salary per Department · Revenue Share by Category (Window %) · Engagement Streaks (3+ Orders, ≤7-Day Gaps) · Top Spender per Membership Tier · Order Funnel Conversion · Month-over-Month Revenue Growth · How Concentrated Is the Spend?.
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Interviewing at more than one company? The same patterns carry: Stripe · Wise · Revolut · Plaid — or the full company-by-company interview guide.