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Why These 75?

Hand-curated for maximum interview ROI.

A 7-Round Onsite Simulator

Seven stages that map exactly to the real DA onsite flow — phone screen → joins → analytics → production patterns → business scorecards → behavioral SQL → edge-case pitfalls. You train for the interview, not a flat problem list.

All 15 Industry Schemas

Every premium domain covered — Stripe-style payments, Airbnb-style lodging, Netflix-style streaming, Meta-style social graph, plus 11 more. Max 6 problems per dataset keeps the practice broad across industries.

Business Scorecards — The Round That Decides Offers

Stage 5 is built specifically for the multi-CTE scorecard questions companies use to separate strong DAs from average ones. Real schemas, real ambiguity, real interview shape.

Skill Coverage

How the 75 problems distribute across SQL topics.

Basic Filtering & Sorting
10
Multi-Table Joins (2-5 tables)
12
GROUP BY + Conditional Aggregation
5
Window Functions (RANK/ROW_NUMBER/LAG/NTILE/SUM OVER/AVG OVER)
10
CTEs (single, multi-CTE, reference value)
5
Subqueries (EXISTS, NOT IN, scalar)
5
Date Arithmetic (JULIANDAY)
3
CASE WHEN Classification
2
Set Operations (UNION, EXCEPT)
2
Multi-CTE Business Scorecards
10
Behavioral Business Scenarios
8
Edge Cases & Pitfalls
3

FAQ

Candidates preparing for data analyst, product analyst, business analyst, and analytics-engineering interviews at top companies.

Specifically: Amazon, Meta, Google, Stripe, Airbnb, Netflix, Uber, DoorDash, LinkedIn, Microsoft, Apple, Salesforce — and 50+ other consumer-tech, fintech, and SaaS companies.

This is the complete SQL prep path; expect ~30 hours of focused work over 4-6 weeks.

Ready to Master SQL?

Start with Stage 1 — graded instantly in your browser.

Start Problem 1Browse All Problems

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LDS Data Analyst Interview Prep 75 — 75 Curated SQL Problems

A 7-stage SQL roadmap built around the patterns that actually decide data analyst loops: multi-table joins, conditional aggregation, window functions, the multi-CTE scorecards that show up in final-round take-homes, and the behavioral "write a query that…" phrasing interviewers actually use. Seventy-five problems across 15 production-grade schemas modeled after Amazon, Stripe, Airbnb, Meta, and Netflix.

Problems included in LDS Data Analyst Interview Prep 75

  1. Premium Products Within a Segment
  2. Active Action Movies
  3. High-Value US Orders (Shipped/Delivered)
  4. Admins and Owners With MFA Enabled
  5. Recent Non-Deleted Direct Messages
  6. Active Search Campaigns by Budget
  7. Active PPO Plans With Prescription Coverage
  8. Open High-Rated Restaurants
  9. Active Sale Listings
  10. Active Users in Target Countries
  11. Reservations With Property Details
  12. Orders With Customer Details
  13. Orders With Customer Details
  14. Active Campaigns With Advertiser Industry
  15. Transactions With Account and Customer Details
  16. User Total Portfolio Value
  17. Bookings With Guest and Room Info
  18. Deliveries With Courier Info
  19. Trips With Rider Details
  20. Charges With Customer and Payment Method
  21. Order Items With Product Info
  22. Customers Without Any Support Tickets
  23. Trade Volume by Security
  24. Average Monthly Bill by Customer Segment
  25. On-Time Delivery Rate by Carrier
  26. Top 5 Riders by Total Spending
  27. Rank Campaigns by Spend per Advertiser
  28. User Sequence Number Within Organization
  29. Transaction Amount Change With LAG
  30. Daily Revenue Running Total per Property
  31. Monthly Watch Hours Moving Average
  32. Highest Rated Delivery per Courier
  33. Charge Amount Quartile Analysis
  34. Top 5 Agents by Listing Count
  35. Rank Accounts by Balance Within Account Type
  36. Provider Claim Rank Within Specialty
  37. Rank Users by Follower Count
  38. Advertisers With High Credit Utilization
  39. Patients Above Plan Average Paid Amount
  40. Promo Code Effectiveness Report
  41. Users With Both Inquiries and Tours
  42. Sellers With Orders But No Returns
  43. Late Deliveries by Carrier and Delay Duration
  44. Dispute Resolution Time Analysis
  45. Overdue Invoices by Aging Bucket
  46. Content Moderation Risk Score
  47. Properties With Bookings or Reviews
  48. Providers with Denied Claims
  49. Campaigns Above Average Spend
  50. Properties Priced Above Average
  51. Plans Never Subscribed
  52. Public Posts Above Average Reactions
  53. Campaign Health Scorecard
  54. Customer Relationship Depth Summary
  55. Property Operations Dashboard
  56. Warehouse Operations Dashboard
  57. Merchant Health Scorecard
  58. Marketplace Operations Dashboard
  59. Customer 360 Summary
  60. Customer Churn Risk Assessment
  61. User Engagement Score Pipeline
  62. Customer Lifecycle Stage Classification
  63. Margin Utilization Risk Report
  64. Cancellation and No-Show Financial Impact
  65. Carrier Performance Report
  66. Rider Behavior Analysis
  67. Refund Reason Analysis
  68. Return Financial Impact Analysis
  69. Subscription Renewal Risk Assessment
  70. Claim Adjudication Pipeline Analysis
  71. Loan Payment Interval Analysis
  72. Daily Price Rank by Volume
  73. Cancellation Root Cause Analysis
  74. OTA Commission vs Direct Booking Analysis
  75. Subscription Revenue Rank Within Plan Type