Master the SQL patterns Netflix asks across Data Analyst, Data Scientist, and Data Engineer roles. Practice A/B tests, churn modeling, and streaming metrics with real datasets and AI tutoring.
21 challenges
Netflix patterns
Real datasets
Streaming & subscriptions
AI tutor
Step-by-step hints
What this page is: we have no dated public source for how Netflix 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 Netflix works with. What is ours: the questions are SQL Quest challenges and the topic emphasis is our editorial judgement, not a measured breakdown of Netflix's interview. Pages with dated sources say so in this spot.
The 21 challenges tagged Netflix in the SQL Quest bank, with every raw challenge tag resolved to the 9 canonical skills. Each share is the portion of those 21 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 Netflix’s interview.
These are SQL Quest challenges chosen because their SQL matches the work — Querying Basics, Aggregation & Grouping, Subqueries & CTEs. They are not questions Netflix 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 Netflix and not a description of its process. We have no dated, citable source for how Netflix 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 Netflix set leans on, one at a time: GROUP BY exercises · CTE practice · Window function practice · JOIN practice · Date function practice — or browse every SQL practice question.
Every question in the Netflix set, one page each with the schema and a hint: Recent Hit Movies (2010s) · Movie Rating Tier Breakdown · Genres Without Blockbusters · Quarterly Hiring Cohort Report · Highest Rated by Genre · Multi-Month Active Customers · Director Rating Volatility · Cumulative Distinct Customers Over Time · Year-over-Year Growth · 7-Day Rolling Revenue Average · Order Sessionization by Customer · Cumulative Revenue Share (Pareto) · Moving Average with Dynamic Window · Rank Movies by Rating · Genres in Common (INTERSECT) · 3-Movie Rolling Average Revenue · RANK vs DENSE_RANK: Rating Gaps · Sliding Window Max Revenue · Customer Retention Cohort · Year-over-Year Movie Rating Trends · Earliest Movie per Genre.
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Interviewing at more than one company? The same patterns carry: Meta · Google · Amazon · Apple — or the full company-by-company interview guide.