Question 1

Is this collection affiliated with Netflix?

Accepted Answer

**No.** This collection is not affiliated with, endorsed by, or sponsored by Netflix.

The 30 problems are designed to mirror the analytical patterns **publicly reported** in Netflix SQL interviews — sourced from our curated catalog, curated down to the 30 best-matched problems for Netflix's data loop. Verified across DS-Analytics, DS-Inference, DS-Algorithms, DE, and Analytics Engineer candidate reports from 2025–2026 (InterviewQuery, Prepfully, Glassdoor, Exponent, interviewing.io, datainterview.com, DataLemur, sql-practice.online, Levels.fyi).

Production-grade schemas are modeled on **Netflix's primary data surface**: streaming media — users, plans, subscriptions, devices, titles, episodes, playback sessions, payments, ratings, and watchlist.

*"Netflix-style" describes the format and pattern coverage, nothing more.*

Question 2

Which data roles does this collection prepare me for?

Accepted Answer

**Primary fit:**
- **Senior Data Scientist — Analytics** — Netflix's highest-volume DS hire; SQL + product sense are roughly half the loop
- **Senior Data Scientist — Inference / Experimentation** — Netflix's iconic role (confirmed on aijobs.net, startup.jobs, Netflix Research). Stage 4 is built specifically for this round
- **Senior Data Scientist — Algorithms / Recommendation** — overlaps with Inference; recsys metrics not directly in scope but the experimentation patterns transfer

**Complementary:**
- **Data Engineer** — SQL bar is similar; the DE loop adds Data Modeling and Iceberg/Spark/Mantis questions not covered here
- **Analytics Engineer** — Strong fit for the SQL + experimentation portion; the dbt / dimensional-modeling round is out of scope

**Lower-priority:**
- **Research Scientist / ML Scientist** — Minimal SQL in those loops; this collection is overkill

Stage 1 is universal across all data roles. Stages 2–5 weight toward DS-Analytics / DS-Inference / Analytics Engineer in patterns and business framing.

Question 3

Does Netflix really have a separate Causal Inference round?

Accepted Answer

**Yes — this is Netflix's most distinctive differentiator vs. any other FAANG.**

Netflix maintains a separately-titled *"Senior Data Scientist — Experimentation & Causal Inference"* job posting (current listings on aijobs.net, startup.jobs, OuterJoin). Netflix Research has a dedicated page titled *Experimentation & Causal Inference*, and the Netflix Tech Blog has a post specifically called *"A Day in the Life of an Experimentation and Causal Inference Scientist."* No other FAANG has a separately-titled Causal Inference DS track at scale (Meta has experimentation-focused DS but not as the headline name; Google has Statistical Inference within Research-DS but it's less product-facing).

**What gets tested in this round:**
- A/B test design, diagnosis, and reading from logged data
- **CUPED** (variance reduction with pre-period covariates) — Netflix is one of the documented production users
- **Ratio metrics + delta method** — *plays per user* is a ratio; computing the variance correctly is non-trivial
- **SRM (Sample Ratio Mismatch)** detection before reading the result
- **Network effects / interference** — shared-account behavior
- Heterogeneous treatment effects, quasi-experiments when randomization isn't possible

Stage 4 of this collection (6 problems) is built specifically around this round. **If you only complete one stage, complete this one** — it's the highest-signal round at Netflix.

Question 4

What about Netflix's flat level structure?

Accepted Answer

**The "no levels" mythology is partly outdated.** Per Levels.fyi (Sep 25, 2022), Netflix introduced IC leveling that year — *"ending the era of no leveling."* Software Engineer ladder is now E3–E7; the same logic was extended to data roles, with DS ladders shown as L3–L7 on Levels.fyi.

**However**, the flat-levels culture still shapes hiring signals:
- Visible posted title is typically **"Senior Data Scientist"** or **"Data Scientist 5"** — narrower public surface than Google's L4/L5/L6
- Compensation is heavily base-loaded; **no performance bonus**, base + RSUs are the components
- **Behavioral bar scales with seniority** more than the question set does — same Culture round, but higher "scope of impact" expected

**What scales at senior level (vs. mid-level):**
- Open-ended prompts with no clean answer (*"evaluate this feature — what would you do?"*)
- **Pushback expected** when interviewer's framing is wrong (a candor signal Netflix actively grades)
- Self-direction (*"what would you investigate next?"* rather than scripted follow-ups)
- Less weight on whiteboard syntax-precision than at Google or Amazon

Every Hard and Expert problem in Stages 3, 4, and 5 includes a **"How a senior interviewer would extend this"** section with 3–6 layered follow-ups grounded in real Netflix onsite reports.

Question 5

Why does the Culture & Values stage have zero problems?

Accepted Answer

**Because Netflix's Culture round doesn't test live SQL.** It tests the **Keeper Test**, candor under disagreement, scope of impact, and *Freedom & Responsibility* alignment.

The stage stays in the roadmap so candidates see the complete loop structure (Recruiter Screen → Tech Phone Screen → 4–6 onsite rounds → 1–3 Culture rounds), but it intentionally contains **0 problems** because writing SQL for a behavioral round would be dishonest.

**Per interviewing.io:** *"At Netflix, coding carries the least weight, behavioral carries a lot."* Across the loop, **roughly 50% of total interview time is behavioral or culture** — the heaviest of any FAANG. A single "no-hire" from a culture interviewer can sink the loop, per Netflix's unanimous-hiring *Dream Team* philosophy.

The Culture interviewer might frame a scenario where SQL would be the tool:

> *"Tell me about a time you owned a metric that moved unexpectedly and had to investigate it."*

But you'll be telling the story, not coding the query. For Netflix-values behavioral prep, see external resources — this collection is SQL-only.

Question 6

Why is the Weekly Retention Curve (the weekly-retention-curve question) so heavily emphasized?

Accepted Answer

**Because cohort retention is Netflix's canonical engagement metric.** Subscription cohort retention + churn-hazard reasoning shows up in nearly every reported Netflix data interview (DataLemur, sql-practice.online, datainterview.com). The reason is simple: Netflix's whole business is *did this cohort stick around?* — monthly cohorts, weekly retention curves, revenue-weighted LTV per plan.

Q8295 (Weekly Retention Curve by First-Watch Cohort) anchors users to their first watch event and then computes return-in-week-N rates — the canonical Netflix "is this cohort sticky?" question. The explainer includes the senior-level probing follow-ups Netflix interviewers specifically ask:

> *"What if a user signs up but watches for the first time three weeks later?"*
> *"How do you handle resubscribers — same user_id or new cohort?"*
> *"What's the right denominator — week-0 cohort size, or week-0 active users?"*

Pair it with **the subscriber-LTV question (Subscriber LTV Report)** for the revenue-side view and **the churn-risk-scoring question (User Churn Risk Assessment)** for the hazard-rate framing. Together they form the retention-and-LTV trio Netflix data scientists work on daily.

Question 7

What's CUPED and why does it keep coming up in Netflix interviews?

Accepted Answer

**CUPED (Controlled-experiment Using Pre-Experiment Data)** is a variance-reduction technique that adjusts post-experiment outcomes using a pre-experiment covariate (typically the same metric measured before the experiment started). The result: tighter confidence intervals, faster experiments, smaller sample sizes needed.

**Netflix is one of the documented production users** alongside Microsoft, Booking, Meta, and Airbnb (sources: Bytepawn, Microsoft Research). Because Netflix runs experiments on a 280M+ subscriber base where every percentage point matters, variance reduction is operationally critical — and that operational reality makes CUPED a recurring interview topic.

**What you should be able to discuss verbally (Stage 4 SQL doesn't ask you to code CUPED end-to-end, but the senior probing does):**
- Why a pre-period covariate reduces variance (it absorbs cross-user heterogeneity)
- The CUPED-adjusted-mean formula at a conceptual level
- Why CUPED fails or under-performs (no good pre-period covariate, treatment shifts the relationship)
- Stage 4 problems (the plan-tier A/B question, the sample-size adequacy question, the weekly-retention-curve question, the churn-risk-scoring question, the subscriber-LTV question) ship with CUPED-adjacent follow-ups in their explainers

The interview pattern: **you'll see a basic A/B read in SQL, then the interviewer will ask how you'd reduce variance — CUPED is the expected answer.**

Question 8

How does this transfer to Disney+, HBO Max, Hulu, Spotify, and other streamers?

Accepted Answer

**Directly. The streaming data surface is shared across all of them.** Every question is tagged with 3–5 peer companies from the streaming ecosystem. The schemas (streaming-media schema: users, plans, subscriptions, devices, titles, episodes, playback sessions, payments, ratings, watchlist) match what streaming analytics engineers touch daily across:

| Streaming platform | What overlaps |
|---|---|
| Disney+ / Hulu / Star+ | ~95% — same subscription + playback + catalog model |
| HBO Max / Paramount+ / Peacock | ~90% — same SVOD shape; tier mix differs |
| Amazon Prime Video / Apple TV+ | ~85% — same subscription model with retailer/device add-ons |
| Spotify / Apple Music | ~75% — playback + subscription transfer; catalog model differs (music vs. video)
| YouTube / YouTube TV | ~70% — same playback event stream; UGC vs. licensed catalog
| Roku / Tubi / Crunchyroll | ~70–80% — depending on AVOD vs. SVOD mix

The weekly retention curve (the weekly-retention-curve question), A/B test (the plan-tier A/B question, the sample-size adequacy question), subscriber LTV (the subscriber-LTV question), churn risk (the churn-risk-scoring question), and multi-CTE scorecard patterns are *language-of-streaming-data* across the entire ecosystem.

Question 9

What's the difference between this and the LDS Google-Style SQL 30?

Accepted Answer

Both are FAANG simulators with overlapping experimentation roots. The differences are real:

| Dimension | Netflix | Google |
|---|---|---|
| **Domain** | Streaming media (subscriptions, playback, catalog) | Digital advertising (Ads, AdSense, AdMob, YouTube Ads) |
| **Distinctive round** | **Experimentation & Causal Inference** (CUPED, ratio metrics, delta method) | **Statistics & Probability** (lift, sample size, percentile distribution) |
| **Signature pattern** | Weekly retention + A/B reads on engagement + subscriber LTV | Sessionization (30-min idle) + D7 cohort retention + A/B significance |
| **Vocabulary** | Recruiter Screen, Tech Phone Screen, SQL/Metrics Deep Dive, Experimentation & Causal Inference, Culture/Values | Tech Phone Screen, SQL & Data Modeling, Stats & Probability, Googleyness |
| **Behavioral weight** | **~50% of loop** — heaviest of any FAANG | ~20% (one Googleyness round) |
| **Level probing** | Narrow visible surface; probing scales on *causal thinking* depth | Strong L4/L5/L6 depth scaling on same question; BigQuery cost probes |
| **Coding weight** | Lightest of the four FAANGs | Heavy |

There's deliberate QID overlap on foundational patterns (e.g., A/B reads, percentile distribution). If you're targeting both companies, do the Netflix collection for the streaming/subscription domain and the Google collection for the ad/event-stream domain — the rounds and probes are different enough that both are worth your time.

Question 10

How long does it take to complete all 30?

Accepted Answer

**Roughly 11 hours of focused practice** across 7–10 days at 4–5 problems per day.

- **Stage 4 (Experimentation & Causal Inference)** takes the most time per problem — 25–35 minutes each for the d3/d4 A/B + retention + churn + LTV problems (especially the subscriber-LTV question Subscriber LTV, which has the deepest multi-CTE revenue logic)
- **+10 minutes per problem** if you do the senior-level causal-thinking follow-ups (highly recommended for Senior Data Scientist candidates)
- **Senior candidates** should plan **13–15 hours total**

The collection is **free up through Stage 1** (5 of 30 problems). The remaining 25 are premium — concentrated where the Netflix-discriminator content lives (causal inference, weekly retention, subscriber LTV, multi-CTE scorecards).

Question 11

How does this compare to DataLemur, StrataScratch, or LeetCode for Netflix interview prep?

Accepted Answer

Tagged-question catalogs help you drill Netflix-style problems. But Netflix's data loop isn't a problem list — it's a **6-round process** with the rare Causal Inference round.

**What LDS Netflix-Style SQL 30 gives you that they don't:**

- **A 6-round Netflix data onsite simulator** — Tech Phone Screen → SQL & Data Modeling → **Statistics & Probability** → **Causal Inference** (Netflix's signature — no other FAANG has this round at scale) → **Product Sense & Metrics** → Culture & Values (intentionally empty)
- **A streaming-media schema modeled on Netflix's actual data surface** — subscriptions, playback sessions, titles, episodes, watchlist, payments
- **The Netflix-signature patterns built into the curriculum** — weekly retention curves, churn-risk scoring, A/B reads on watch behavior, subscriber LTV
- **Causal-inference probing follow-ups** in every d3/d4 explainer — CUPED variance reduction, ratio-metric delta method, interference between shared accounts, SRM checks
- **Both SQLite and PostgreSQL** on every problem

*Catalogs give you Netflix-tagged questions. This collection gives you the Netflix interview itself.*

LDS Netflix-Style SQL 30

About this collection

Your Learning Path

Why These 30?

A 6-Round Netflix Onsite Simulator

A Dedicated Causal Inference Round

A Streaming-Media Schema Modeled on Netflix

Skill Coverage

FAQ

Ready for your Netflix interview?

LDS Netflix-Style SQL 30

About this collection

Your Learning Path

Why These 30?

A 6-Round Netflix Onsite Simulator

A Dedicated Causal Inference Round

A Streaming-Media Schema Modeled on Netflix

Skill Coverage

FAQ

Ready for your Netflix interview?

LDS Netflix-Style SQL 30

About this collection

Your Learning Path

Why These 30?

A 6-Round Netflix Onsite Simulator

A Dedicated Causal Inference Round

A Streaming-Media Schema Modeled on Netflix

Skill Coverage

FAQ

Ready for your Netflix interview?

LDS Netflix-Style SQL 30 — 30 Curated SQL Problems

Problems included in LDS Netflix-Style SQL 30

LDS Netflix-Style SQL 30

About this collection

Your Learning Path

Why These 30?

A 6-Round Netflix Onsite Simulator

A Dedicated Causal Inference Round

A Streaming-Media Schema Modeled on Netflix

Skill Coverage

FAQ

Ready for your Netflix interview?