Screening round is made of 3 interviews: hr call, coding, manager. First one is pretty standard, they just wanna know why you applied and your career, second is a mix of python and sql applied to a simple data model about netlfix where you have to prove you can code basically.
Third is a catchup with the hiring manager and you are asked more in depth questions about what you have worked on in the past. If you pass the screening phase then you have the second round: 2 coding interviews and another HR interview where they go in depth about their culture.
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One coding round is with a data engineer more focused on the analytics, so it's similar to the coding interview of the screening round, they just go more in depth. The other coding interview is done by a software engineer and they wanna test your algo and data structures skills, it's not leetcode but still you are asked an algorithmic question related to a problem you might face at netflix.
Last round should be other 2/3 more interviews with stakeholders and engineering director, but I did not make it here so that's all i know.
Netflix Data Engineer Interview Guide
Because the bar is senior by default, the loop weights system design, judgment, and communication heavily, and the SQL round is done live in a shared editor against a described schema (viewing data, subscriber events, A/B results) with clean, well-tested, documented code expected. There is little room for a junior profile; the loop is calibrated to people who already operate at a senior level elsewhere. The culture interview carries weight equal to the technical rounds.
The Netflix interview timeline
First contact to offer, stage by stage, with how long each round runs and roughly when it lands.
- 1wk 030 minRecruiter Screen
- 2wk 1-260 minTechnical Screen
- 3wk 34 to 5 hoursOnsite (Virtual Loop)
Netflix data engineer interview process
The loop stage by stage, from recruiter call to offer.
- 01
Recruiter Screen
Conversational call about your background, interest in Netflix, and experience with large-scale data platforms. Netflix operates on a freedom-and-responsibility culture, so the recruiter probes for self-direction and independent judgment. Expect questions about scale and your comfort level owning entire pipelines end to end. The recruiter also confirms you are targeting a senior-level role, since Netflix does not hire junior data engineers.
- ▸Emphasize ownership: Netflix DEs own pipelines from ingestion to serving
- ▸Mention streaming data experience if you have it; Netflix processes billions of playback events daily
- ▸Research which team you are interviewing for: Data Platform, Content Analytics, Streaming, or Personalization
- ▸Be ready to discuss your compensation expectations; Netflix is transparent about their all-cash model
- 02
Technical Screen
A coding exercise focused on data manipulation, usually in Python or SQL. Netflix favors Spark-heavy roles, so expect questions about transformations on large datasets. You may be asked to write PySpark or reason about distributed processing. The interviewer evaluates your ability to think about partitioning, shuffles, and data skew. Some screens include an Iceberg or streaming component.
- ▸Be comfortable writing PySpark: groupBy, window functions, joins with broadcast hints
- ▸Discuss tradeoffs: why a particular join strategy matters at Netflix scale
- ▸If given SQL, expect complex multi-step problems with streaming event data
- ▸Know Iceberg basics: hidden partitioning, time travel, schema evolution
- 03
Onsite (Virtual Loop)
4 to 5 sessions covering system design, coding, data modeling, and culture fit. Netflix interviews are conversational and collaborative. System design focuses on real-time data pipelines for content recommendations, A/B testing platforms, or streaming quality analytics. The culture interview carries significant weight and evaluates alignment with the Netflix culture memo. Expect at least one round on distributed systems and one dedicated to behavioral questions about autonomy and decision-making.
- ▸Read the Netflix culture memo before the interview; interviewers expect familiarity
- ▸System design should reference Kafka, Flink or Spark Streaming, and Iceberg
- ▸Prepare examples of making tough calls independently, since Netflix values informed captains
- ▸The culture round is weighted equally with technical rounds; do not underprepare it
What the Netflix loop tests: domains and difficulty
Our prediction of the question mix by domain and difficulty for this company's data engineer loop, from live listings and interview reports.
The domain and difficulty mix we predict for a Netflix data engineer loop, across 13 problems. It updates as more Netflix data lands.
6 real Netflix interview questions
Reported by candidates from real loops, tagged by domain, round, level, and year. Expand for what the round is scoring.
SQLL5 · 2025Compute first-touch attribution for each converted user: given a table of user sessions with touch events and a conversions table, identify the first marketing touch for every user who ultimately converted.Onsite · sql+
Schema: attribution table with user_id, touch_type, touch_date; user_sessions table with user_id, session_id, converted (boolean). Expected approach: CTE to rank touches per user by date, filter for rank=1, join with conversions table. Requires understanding of window functions and join semantics. Source provided no additional schema constraints beyond what is listed.
SQLL4 · 2024Given a subscriptions table with user_id, start_date, and end_date columns, detect cases where a single user has overlapping subscription periods.Onsite · sql+
Schema: subscriptions(id, user_id, start_date, end_date). A subscription overlap exists when two rows for the same user have a date range intersection: start_a < end_b AND start_b < end_a. Expected approach: self-join on user_id where s1.id < s2.id and date ranges overlap. Alternative approach uses LAG() to compare adjacent periods after ordering by start_date within each user partition. Source from Netflix data engineer interview guide.
PythonL5 · 2024Given a list of (start_time, end_time) tuples representing user video view sessions that may overlap, return the merged list of unique non-overlapping time ranges actually watchedPhone screen · screen python+
PythonL4 · 2024Write a Python function to calculate quantiles of a given list, including the median (p50) and arbitrary percentile values such as p60.Phone screen · screen python+
Netflix Data Engineering first technical round (phone screen), March 2024. The question asked for a Python function to calculate quantiles from a given list—specifically the median and arbitrary percentiles such as the 60th percentile—without specifying allowed libraries. Expected approach: sort the list, then index into the sorted list based on the percentile fraction. The same round also included verbal questions about Spark distributed computing: broadcasting, narrow vs. wide transformations.
Data modelingL5 · 2025design star schema and some easy leetcode type problem. fairly easy stuff to code some sql and python. sql had some window functions and pythong with dictionaries. design and query this star schema for subscriptionsOnline assessment+
Data modelingL5 · 2025Design a database schema for recording car timing data on the Golden Gate Bridge, then write queries to answer questions such as 'which car was fastest today'.Onsite · data modeling+
This is a classic data modeling + query design question. Schema must capture: vehicle identifier, timestamp of crossing, direction (inbound/outbound), speed or duration. Follow-up queries test window functions (RANK/DENSE_RANK by speed per day). Expected tables: crossings(vehicle_id, timestamp, entry_speed, direction) or similar. Interviewer evaluates normalization decisions and query efficiency. Source provided no additional detail.
Recent Netflix interview reports
Candidate accounts of the loop, each with its date, level, difficulty, and outcome. Scroll the feed.
4 candidate interview reports
real candidate submissions
design star schema and some easy leetcode type problem. fairly easy stuff to code some sql and python. sql had some window functions and pythong with dictionaries. design and query this star schema for subscriptions
Hi, I just got reached by Netflix HR. Can someone share how does the Netflix DE Onsite rounds looks like. And what they focus on, for eg. is it Data Modelling, Data Architecture, Data Pipeline etc.
Hi, I recently cleared the Netflix onsite. Can someone help me with what all rounds would onsite have. And what they focus on most. Everyone company is different, some focus in data modelling, some ETL archtecture, some Data pipeline. Really want to crack this. Thanks
Try a Netflix-style SQL round
Find every user active on 3 or more CONSECUTIVE days. This gaps-and-islands shape shows up in nearly every DE SQL round. Edit the query and run it against the seed data.
Practice the Netflix loop
The problems our model expects in this company's interview, grouped by round. Work the shapes that come up, not the ones that read well on a list.
Common mistakes in Netflix DE interviews
The patterns that sink otherwise strong candidates here.
Preparing for a junior-level interview
Netflix only hires at senior level and above for data engineering. Every question assumes years of production experience with distributed systems. If you cannot discuss Spark internals, Kafka partition strategies, or schema evolution from real projects, you are not ready.
Ignoring the culture interview
Candidates who ace every technical round still get rejected on culture fit. The culture round carries equal weight. Read the Netflix culture memo, prepare 5 to 6 specific stories that demonstrate judgment, candor, and ownership, and practice telling them concisely.
Designing systems at the wrong scale
Netflix operates at hundreds of millions of users and billions of daily events. Designing a pipeline that works for 10 million rows will not impress anyone. Always start with scale: how many events per second, how much data per day, what latency is acceptable.
Not knowing Iceberg
Netflix invented Apache Iceberg. Candidates who only know Hive-style partitioning or have never worked with a modern table format stand out negatively. Learn hidden partitioning, time travel queries, schema evolution, and the difference between copy-on-write and merge-on-read.
Giving generic behavioral answers
Netflix interviewers are trained to detect rehearsed stories that could apply to any company. Your examples must show autonomous decision-making, comfort with ambiguity, and willingness to take calculated risks. 'I followed the process my manager set up' is the wrong answer.
Expecting stock or equity in the offer
Netflix pays entirely in cash. There are no RSUs, no stock options, no vesting schedules. If you ask about equity during negotiations, it signals you have not researched the company. Know the comp model before you get to the offer stage.
Netflix-specific preparation tips
Tactical advice for the dimensions this company weighs.
Netflix runs on freedom and responsibility
The culture memo is not a formality. Interviewers check whether you operate well with minimal process. Describe situations where you identified a problem, proposed a solution, and executed without being told. Avoid stories where you waited for approval.
Spark expertise is expected, not optional
Netflix is one of the heaviest Spark users in the industry. Know Spark internals: catalyst optimizer, shuffle mechanics, broadcast joins, partition pruning. Be ready to debug a slow Spark job on a whiteboard.
Iceberg is central to their data platform
Netflix co-created Apache Iceberg and uses it extensively. Understand table formats: why Iceberg over Hive-style partitioning, time travel, schema evolution, and hidden partitioning. This differentiates you from candidates who only know warehouse-style storage.
Streaming data is the default context
Most Netflix DE questions are framed around event streams: playback events, UI interactions, quality metrics. Think in terms of event-time processing, late arrivals, and exactly-once delivery. Batch is the exception, not the rule.
Show strong opinions, loosely held
Netflix values engineers with technical opinions. When you make a design decision in a system design round, state your rationale clearly, for example choosing a log-based stream over a queue because you need replay capability and ordered consumption. When the interviewer challenges your decision, either defend it with evidence or update your position gracefully. Stubbornness and passivity are both red flags.
Expect open-ended prompts by design
Netflix interviews reflect their culture: you get context, not control. System design prompts will be vague on purpose. The interviewer wants to see you define the problem before solving it. Candidates who immediately start drawing architecture without asking questions or defining scope signal that they need too much direction.
What Netflix is really evaluating
The signals behind the questions. Shape every answer around these.
Freedom and Responsibility
Netflix gives engineers extraordinary autonomy, but expects proportional accountability. You choose your tools, set your priorities, and own the outcome. In interviews, demonstrate situations where you operated with minimal oversight and delivered results. Avoid stories where you needed step-by-step guidance.
Context, Not Control
Managers at Netflix provide context (goals, constraints, timelines) rather than instructions. Engineers are expected to figure out the how. In system design rounds, show that you can take a vague business goal and translate it into a concrete technical plan without being told exactly what to build.
The Keeper Test
Netflix managers regularly ask themselves: if this person wanted to leave, would I fight hard to keep them? This is the bar. It means Netflix only retains high performers and expects every engineer to continuously raise the bar. In interviews, signal that you raise the performance of teams you join.
Radical Candor
Netflix values direct, honest feedback at every level. Interviewers may challenge your design decisions to see how you respond. Do not get defensive. Engage with the feedback, adapt your design, and demonstrate that you welcome being wrong when someone has a better idea.
Highly Aligned, Loosely Coupled
Teams at Netflix share strategic context but operate independently. Data engineers need to build systems that integrate cleanly with other teams without tight coupling or constant coordination. Your behavioral answers should demonstrate building interfaces, APIs, and data contracts that let teams work independently while staying aligned on shared goals.
Netflix is hiring data engineers now
The roles behind this loop. Prep against the levels and locations they are actually filling.
Architect, strengthen, and expand the core data products that scale our Ads business.
Java, Scala, Python) and comfortable working with SQL
Build both software and social glue across a wide network of stakeholders—VPs, Directors, Managers, and ICs—enabling decisions that affect hundreds of millions of members and major content and marketing investments.
Data Engineering at Netflix is a role that requires building systems to process data efficiently and modeling the data to power analytics.
The team will also build the automated systems integration test framework (for batch and streaming connectors).
Experience with Spark, Flink, Iceberg, and Kafka is helpful but not strictly required. * Proficiency in Python and/or Scala for scripting, automation, and data orchestration frameworks, as well as the ability to write complex SQL (any variant) for ad-hoc and recurring workflows.
This expansion reinforces our mission to boost Netflix engineers’ productivity and innovation by providing a robust, efficient, and secure data platform, while fostering close collaboration between data platform teams to deliver greater organizational throughput and agility.
Netflix compensation and culture
The numbers, tech stack, and team structure live on the company overview.
Netflix DE interview FAQ
How many rounds are in a Netflix DE interview?+
Does Netflix hire junior data engineers?+
Does Netflix focus on SQL or Spark more for DE roles?+
What is the Netflix culture interview like?+
What is the keeper test and should I worry about it?+
How long does the Netflix interview process take?+
Do I need to know Apache Iceberg for the Netflix interview?+
Is the Netflix culture interview really that important?+
Netflix data engineer roles by level
Level-specific pages: the comp, the bar, and what the loop tests at each seniority.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Compare Netflix with other data engineering employers
How the role, pay, and loop stack up against peer companies.
Prepare at Netflix interview difficulty
- 01
Reading a solution is not the same as writing one
Every engineer who has frozen on a query they had read a dozen times knows the gap. The only preparation that closes it is producing the answer yourself, under time, before the interview does it for you
- 02
76% of hiring managers reject on the coding task, not the resume
From HackerRank's 2024 Developer Skills Report. Candidates who look strong on paper still fail the live screen if they haven't done timed, executable practice
- 03
5 problem shapes cover 80% of data engineer loops
Dedup, sessionization, top-N-per-group, slowly-changing dimensions, partition tricks. Writing the shapes by hand turns the unfamiliar into pattern recognition