Plaid moves messy bank data. Practice transaction dedup, NULL handling, merchant categorization and data-quality SQL on a synthetic card-transaction ledger and the app’s ecommerce tables, with an AI tutor when you get stuck.
34 challenges
Plaid-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 Plaid 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 Plaid works with. What is ours: the questions are SQL Quest challenges and the topic emphasis is our editorial judgement, not a measured breakdown of Plaid's interview. Pages with dated sources say so in this spot.
The 34 challenges tagged Plaid in the SQL Quest bank, with every raw challenge tag resolved to the 9 canonical skills. Each share is the portion of those 34 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 Plaid’s interview.
These are SQL Quest challenges chosen because their SQL matches the work — Querying Basics, Aggregation & Grouping, Joins. They are not questions Plaid 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 Plaid and not a description of its process. We have no dated, citable source for how Plaid 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 Plaid set leans on, one at a time: GROUP BY exercises · JOIN practice · CTE practice · CASE WHEN practice · Date function practice — or browse every SQL practice question.
Every question in the Plaid set, one page each with the schema and a hint: Membership Display Labels · Non-Sales Roster (NOT IN) · How Many Genres Do We Cover? (DISTINCT) · Average Salary by Department · High-Volume Categories (HAVING) · Position of '@' in Email (INSTR) · Transaction Share by Merchant Category · Chargeback Reason Codes: Resolved and Still Open · The Ledger's First Week, Day by Day · Customers Who Never Ordered · Fare Imputation Analysis · UNION ALL Dedup: Cross-Dataset Search · LEFT JOIN NULL Semantics: Inactive Customers · Category Revenue with Relabeling · Find Duplicate Emails · Family Size Survival Buckets · Department Tenure Span · Month-over-Month Customer Growth · Bonus Tier with Cross-Conditions · Customers Without Orders · Employee + Manager Pairs (Self Join) · Same-Year Hires in Same Department (Self Join) · Country × Category Coverage Matrix (Cross Join) · Dormant Cards — No Transactions in the Last 14 Days · Spend and Disputes Per Cardholder — Without the Fan-Out · First and Last Order per Customer · Revenue Share by Category (Window %) · Self-Join: Manager Salary Comparison · Top-N Products per Category · Deduplicate Orders with ROW_NUMBER · Anti-Join Pipeline: Unmatched Records · Customers with Orders in ALL Categories · Recursive Org Chart Traversal · Two Swipes at the Same Merchant Inside a Day.
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Interviewing at more than one company? The same patterns carry: Stripe · Wise · Revolut · Ramp — or the full company-by-company interview guide.