Top 22 Netflix Data Engineer Interview Questions + Guide in 2024

With over 260 million subscribers in Q4 2023, Netflix holds the position of the most popular streaming platform. Its popularity, in addition to offering a diverse range of content, is fueled by its recommendation engine, which employs data engineering algorithms to efficiently increase average user watch time.

As a potential candidate, you’ll need to understand the Netflix interview process, be able to answer common Netflix data engineer interview questions, and follow a few tips to crack the data engineering interview.

Netflix relies heavily on user experience and personalized recommendations to drive its business forward. It also tremendously values its culture and wants employees to fit in.

As a data engineering candidate, you may expect a thorough interview process, consisting of multiple rounds of behavioral, technical, and take-home questions.

Here is how it usually goes:

Submitting the Application

You can submit your application for the Netflix Data Engineer role through employee referrals, attending its community events, and applying through the career portal. As Netflix doesn’t offer bonuses for employee referrals, recommendations are scarce and effectual. It may secure an audience with the recruiter, but that won’t relax the interview or decision-making process.

Initial Recruiter Call

You’ll receive an initial recruiter call designed to assess your technical skills, experience, and cultural fit. This may involve submitting a resume, cover letter, and relevant work samples demonstrating proficiency in database management, pipeline development, and programming languages such as Python or R. You’ll also be asked a few behavioral questions during this round.

Hiring Manager and Technical Interview

Being successful in the previous round, you’ll move on to the technical interview phase, where you’ll be put to the test for your problem-solving abilities and technical expertise. Netflix is known for its rigorous technical interviews, which typically involve a series of coding challenges, algorithmic problems, and practical complexities.

The Hiring Manager over at Netflix, who would likely be your boss if you get the job, may also ask a few technical and behavioral questions regarding the Data Engineer role.

These interviews delve deep into your understanding of software engineering principles, DBMS, and algorithms.

On-Site Interview

Depending on the seniority of the role you’re applying for, expect at least half a day of interviews with four to eight people during the on-site interview round, including the Hiring Manager. During these rounds, the interviewers will share feedback with the Hiring Manager so they can make a decision regarding your candidacy.

An extremely talented team of Data Engineers over at Netflix maintain data pipelines, design data warehouses, and manages dabase systems. Here are a few questions that are often asked in the Data Engineer role at Netflix:

1. What makes you a good fit for our company?

The interviewer will assess whether you’re a fit for Netflix’s culture, values, and Data Engineer role requirements with this question.

How to Answer

Highlight your relevant skills, experiences, and alignment with the company’s mission and values.

Example

“I believe my background in data engineering, particularly in developing data pipelines, aligns well with Netflix’s focus on leveraging data to enhance user experience and content delivery. Additionally, my experience working in cross-functional teams and my ability to adapt to fast-paced environments make me well-suited for the dynamic culture at Netflix.”

2. How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?

This question assesses your time management and organizational skills necessary to work as a Data Engineer at Netflix, especially in handling multiple tasks simultaneously.

How to Answer

Discuss your approach to prioritization, time management techniques, and organizational tools you use to stay on top of deadlines.

Example

“When faced with multiple deadlines, I start by assessing the urgency and importance of each task. I use techniques like Eisenhower’s Urgent/Important Principle to prioritize tasks effectively. Additionally, I utilize project management tools like Asana or Trello to create task lists, set deadlines, and track progress. By breaking down tasks into smaller, manageable chunks and setting realistic timelines, I ensure that I stay organized and meet all deadlines efficiently.”

3. Give an example of when you resolved a conflict with someone on the job.

You’ll be evaluated based on your conflict resolution skills and ability to handle interpersonal challenges at Netflix through this question.

How to Answer

Describe a specific conflict situation, your approach to resolving it, and the positive outcome.

Example

“In my previous role, there was a disagreement between team members regarding the approach to a project. Some team members preferred a traditional methodology, while others advocated for an agile approach. To resolve the conflict, I facilitated a team meeting where we openly discussed our concerns and perspectives. By actively listening to each team member, acknowledging their viewpoints, and finding common ground, we were able to reach a consensus on a hybrid approach that satisfied everyone. This not only resolved the conflict but also strengthened team collaboration and improved project outcomes.”

4. What would your current manager say about you? What constructive criticisms might he give?

This question evaluates your self-awareness and ability to reflect on feedback necessary to work in a data-driven company such as Netflix.

How to Answer

Reflect on your strengths as mentioned by your manager and areas for improvement based on constructive criticism.

Example

“My current manager would likely commend my strong analytical skills, dedication to delivering high-quality work, and ability to collaborate effectively with cross-functional teams. However, he might also mention that I could improve my delegation skills to better distribute workload among team members and alleviate pressure during busy periods. I value constructive feedback as it helps me continuously grow and improve in my role.”

5. What interests you most about the Data Engineer role at Netflix? What specific challenges and opportunities do you see in this field?

The interviewer will assess your understanding of Netflix’s data engineering initiatives and your insights into the challenges and opportunities in the field.

How to Answer

Highlight your interest in Netflix’s data-driven approach to content recommendation, personalization, and decision-making. Discuss specific challenges in analyzing large-scale streaming data and opportunities for innovation in content delivery and user experience.

Example

“I’m particularly intrigued by Netflix’s data engineering efforts in enhancing content recommendation algorithms and personalizing user experience. The challenge of analyzing vast amounts of streaming data in real-time to understand user preferences and behavior presents exciting opportunities for innovation.

Additionally, Netflix’s focus on leveraging data to inform content creation and acquisition decisions aligns with my passion for utilizing data-driven insights to drive business outcomes. I’m excited about the prospect of contributing to Netflix’s data engineering initiatives and addressing the evolving challenges in this dynamic field.”

6. Given a table containing data for monthly sales, write a query to find the total amount of each product sold for each month with each product as its own column in the output table.

Example:

Input:

monthly_sales table

month	product_id	amount_sold
2021-01-01	1	100
2021-01-01	2	300
2021-02-01	3	200
2021-03-01	4	250

Output:

month	1	2	3	4
2021-01-01	100	0	300	0
2021-02-01	0	200	0	0
2021-03-01	0	0	0	250

This question evaluates your understanding of database design, which are often is used to manage data in the Netflix servers.

How to Answer

Write an SQL query to find the total amount of products sold and sort them in the desired manner.

Example

SELECT
	concat(date, '-01') month,
	sum(
		CASE WHEN product_id = 1 THEN
			counts
		ELSE
			0
		END) AS '1',
	sum(
		CASE WHEN product_id = 2 THEN
			counts
		ELSE
			0
		END) AS '2',
	sum(
		CASE WHEN product_id = 3 THEN
			counts
		ELSE
			0
		END) AS '3',
	sum(
		CASE WHEN product_id = 4 THEN
			counts
		ELSE
			0
		END) AS '4'
FROM (
	SELECT
		date_format(month, '%Y-%m')
		date,
		product_id,
		sum(amount_sold) counts
	FROM
		monthly_sales
	GROUP BY
		date_format(month, '%Y-%m'),
		product_id) temp
GROUP BY
	date

7. Given a list of integers, write a function gcd to find the greatest common denominator between them.

Example:

Input:

int_list = [8, 16, 24]

Output:

def gcd(int_list) -> 8

The interviewer will assess your understanding of basic programming skills through this question.

How to Answer

Approach the problem through Euclidean Algorithm and write a function that returns the greatest common denominator of the entered numbers.

Example

def gcd(numbers):
    def compute_gcd(a, b):
        while b:
            r = a % b
            a = b
            b = r
        return a
    g = numbers[0]
    for num in numbers[1:]:
        g = compute_gcd(num, g)
    return g

8. How would you interpret coefficients of logistic regression for categorical and boolean variables?

Netflix uses data and machine learning to improve recommendation and user experience. This question evaluates your understanding of logistic regression and interpretation of regression coefficients.

How to Answer

Explain that coefficients in logistic regression represent the change in the log-odds of the dependent variable for a one-unit change in the predictor variable for categorical variables and boolean variables.

Example

“In logistic regression, the coefficient for a categorical variable indicates the change in log-odds of the dependent variable when moving from the reference category to the specific category represented by that coefficient. For boolean variables, the coefficient represents the change in log-odds when the variable changes from 0 to 1.”

9. Let’s say you have analytics data stored in a data lake. An analyst tells you they need hourly, daily, and weekly active user data for a dashboard that refreshes every hour. How would you build this data pipeline?

The interviewer at Netflix will consider your approach to real-life data engineering scenarios with this question.

How to Answer

Describe the data extraction tools required for the job and explain how you would approach the requirement. Also discuss how you would store and access the data.

Example

“To construct the requested data pipeline, I would begin by leveraging robust data extraction tools such as Apache Spark or AWS Glue to retrieve the relevant analytics data stored in the data lake. Once extracted, the next step would involve transforming this data to derive the hourly, daily, and weekly active user metrics. This transformation process would entail aggregating the extracted data based on user activity within each specified time period.

Subsequently, the transformed data would be stored in a suitable analytics-optimized data storage solution, such as Amazon Redshift or Google BigQuery, ensuring efficient retrieval and analysis. To ensure seamless and timely updates, I would automate the entire pipeline by setting up scheduled workflows using workflow orchestration tools like Apache Airflow or AWS Step Functions. This automation would ensure that the dashboard refreshes every hour with the most up-to-date active user metrics.”

10. Let’s say we have a table with an id and name fields. The table holds over 100 million rows and we want to sample a random row in the table without throttling the database.

Write a query to randomly sample a row from this table.

Input:

big_table table

Columns	Type
id	INTEGER
name	VARCHAR

This question assesses understanding of large datasets and your ability to manipulate them without overwhelming the servers.

How to Answer

When dealing with a large table with over 100 million rows and needing to sample a random row without throttling the database, it’s important to consider an efficient approach that doesn’t burden the database server with excessive processing.

Example

SELECT r1.id, r1.name
FROM big_table AS r1
INNER JOIN (
    SELECT CEIL(RAND() * (
        SELECT MAX(id)
        FROM big_table)
    ) AS id
) AS r2
    ON r1.id >= r2.id
ORDER BY r1.id ASC
LIMIT 1

11. Given a table of product subscriptions with a subscription start date and end date for each user, write a query that returns true or false whether or not each user has a subscription date range that overlaps with any other completed subscription.

The Netflix interviewer will assess your understanding of SQL queries and database management with this question.

Completed subscriptions have end_date recorded.

Example:

Input:

subscriptions table

Column	Type
user_id	INTEGER
start_date	DATETIME
end_date	DATETIME

user_id	start_date	end_date
1	2019-01-01	2019-01-31
2	2019-01-15	2019-01-17
3	2019-01-29	2019-02-04
4	2019-02-05	2019-02-10

Output:

user_id	overlap
1	1
2	1
3	1
4	0

How to Answer

You’ll need to check if the start date of one subscription falls within the date range of another completed subscription or if the end date of one subscription falls within the date range of another completed subscription.

Example

SELECT
    s1.user_id
    , MAX(CASE WHEN s2.user_id IS NOT NULL THEN 1 ELSE 0 END) AS overlap
FROM subscriptions AS s1
LEFT JOIN subscriptions AS s2
    ON s1.user_id != s2.user_id
        AND s1.start_date <= s2.end_date
        AND s1.end_date >= s2.start_date
GROUP BY 1

12. Write a SQL query to select the 2nd highest salary in the engineering department.

This question will assess your understanding of database management systems and SQL as a Data Engineer.

Note: If more than one person shares the highest salary, the query should select the next highest salary.

Example:

Input:

employees table:

Column	Type
id	INTEGER
first_name	VARCHAR
last_name	VARCHAR
salary	INTEGER
department_id	INTEGER

departments table:

Column	Type
id	INTEGER
name	VARCHAR

Output:

Column	Type
salary	INTEGER

How to Answer

To find the second highest salary in the Engineering department using SQL, first identify the department and select all unique salaries. Then, order these salaries in descending order and select the one following the highest salary. Construct a SQL query incorporating these steps, execute it, and verify the result for accuracy.

Example

SELECT
    salary
FROM (
    SELECT salary
    FROM employees
    INNER JOIN departments
        ON employees.department_id = departments.id
    WHERE departments.name = 'engineering'
    GROUP BY 1
    ORDER BY 1 DESC
    LIMIT 2
) AS t
ORDER BY 1 ASC
LIMIT 1

13. Write a query to identify customers who placed more than three transactions each in both 2019 and 2020.

Example:

Input:

transactions table:

Column	Type
id	INTEGER
user_id	INTEGER
created_at	DATETIME
product_id	INTEGER
quantity	INTEGER

users table:

Column	Type
id	INTEGER
name	VARCHAR

Output:

Column	Type
customer_name	VARCHAR

As a data engineering candidate, you’re expected to answer database-related questions. Your SQL skills, particularly in writing queries with multiple conditions, will be assessed through this question.

How to Answer

Write a SQL query that selects customers who meet the specified criteria.You may also describe the steps as you go forward.

Example

WITH transaction_counts AS (
SELECT u.id,

name,

SUM(CASE WHEN YEAR(t.created_at)= '2019' THEN 1 ELSE 0 END) AS t_2019,

SUM(CASE WHEN YEAR(t.created_at)= '2020' THEN 1 ELSE 0 END) AS t_2020

FROM transactions t

JOIN users u

ON u.id = user_id

GROUP BY 1

HAVING t_2019 > 3 AND t_2020 > 3)

SELECT tc.name AS customer_name

FROM transaction_counts tc

14. What metrics would you prioritize to evaluate the effectiveness of Netflix’s recommendation algorithm, and how would you interpret the results?

The interviewer will assess your understanding of Netflix’s recommendation algorithms and the metrics used to evaluate their effectiveness.

How to Answer

Prioritize metrics such as user engagement, user satisfaction, and business impact. Interpretation involves analyzing these metrics over time and comparing them with benchmarks or A/B test results.

Example

“I would prioritize click-through rate (CTR), session length, user engagement, interactions per session, A/B testing results for recommendation variations, and customer lifetime value to evaluate Netflix’s recommendation algorithm. I’d interpret high CTR and longer session lengths as positive indicators of algorithm effectiveness. Additionally, comparing engagement metrics across different recommendation strategies through A/B testing would provide insights into which algorithm variations drive better user interactions and retention.”

15. How would you design data models to support various analytical queries, such as understanding user engagement or content performance?

Your knowledge of designing and optimizing data warehouses for analytics purposes, essential for deriving insights and making data-driven decisions at Netflix, will be evaluated through this question.

How to Answer

Explain the architecture of a data warehouse suitable for Netflix’s analytics needs, including data modeling techniques like star schema or snowflake schema. Discuss strategies for optimizing data models and queries to support various analytical queries efficiently.

Example

“A robust data warehousing architecture at Netflix involves a scalable storage layer, such as Amazon Redshift or Google BigQuery, for storing structured and semi-structured data. We employ data modeling techniques like star schema to organize data into fact and dimension tables, facilitating efficient query execution for analytics. To optimize query performance, we implement indexing, partitioning, and materialized views, ensuring fast response times for analytical queries. By designing our data warehouse with scalability and performance in mind, we empower Netflix analysts to derive actionable insights from our vast data repository.”

16. Describe the importance of data governance and metadata management in a large-scale data environment like Netflix.

This question evaluates your understanding of data security principles and compliance requirements, crucial for protecting sensitive user data and ensuring regulatory compliance at Netflix.

How to Answer

Discuss strategies for implementing data encryption, access control, and auditing mechanisms to safeguard sensitive user data. Explain how you would ensure compliance with regulations like GDPR and CCPA through data anonymization, consent management, and data retention policies.

Example

“Data security and compliance are top priorities at Netflix to protect user privacy and maintain regulatory compliance. We implement end-to-end encryption for data in transit and at rest to prevent unauthorized access. Role-based access control mechanisms ensure that only authorized personnel can access sensitive data, while auditing trails track data access and modifications for compliance purposes. Additionally, we anonymize user data where applicable to adhere to regulations like GDPR and CCPA, and we manage user consent preferences to respect privacy rights. By adopting a comprehensive approach to data security and compliance, we uphold user trust and legal obligations at Netflix.”

17. Explain the differences between batch processing and streaming processing. When would you choose one over the other for processing Netflix’s user data?

The interviewer at Netflix will evaluate your understanding of real-time data processing and its application in building features like real-time recommendations, through this question.

How to Answer

Explain the differences between batch and streaming processing and discuss when each approach is suitable. Describe how you would implement real-time recommendation systems using technologies like Apache Flink or Apache Storm.

Example

“Streaming data processing is essential for real-time recommendation systems at Netflix, where user preferences need to be analyzed instantly to provide personalized content suggestions. Unlike batch processing, streaming processing enables continuous analysis of data streams, allowing us to update recommendations in real time as user preferences change. Technologies like Apache Flink can be used to process streaming data with low latency, enabling us to deliver timely and relevant recommendations to Netflix users.”

18. Describe the importance of data indexing in optimizing query performance for analyzing large datasets at Netflix.

Big data optimization is a required skill for a Netflix Data Engineers. This question will evaluate your understanding of database management and optimization techniques for analyzing large datasets at Netflix.

How to Answer

Explain the role of data indexing in improving query performance by enabling faster data retrieval. Emphasize its significance in optimizing the efficiency of data analysis processes, especially when dealing with large volumes of data.

Example

“Data indexing plays a crucial role in optimizing query performance for analyzing large datasets at Netflix. By creating indexes on relevant columns, such as user IDs or timestamps, we can significantly reduce the time required to retrieve data, thereby improving the efficiency of query processing. This is particularly important for Netflix, where timely analysis of large volumes of user data is essential for making informed business decisions and enhancing the streaming experience.”

19. Explain the significance of data normalization in maintaining data integrity and consistency in Netflix’s database systems.

This question assesses your understanding of data management principles and their application in maintaining data integrity and consistency at Netflix.

How to Answer

Describe how data normalization reduces redundancy and inconsistency in Netflix’s database systems, leading to improved data integrity and consistency. Highlight its role in minimizing data anomalies and facilitating efficient data storage and retrieval.

Example

“Data normalization is crucial for maintaining data integrity and consistency in Netflix’s database systems. By organizing data into well-structured tables and eliminating redundant information, normalization reduces the risk of data anomalies such as insertion, update, and deletion anomalies. This ensures that data remains accurate and consistent across different parts of the database, enabling reliable analysis and decision-making processes at Netflix.”

20. How would you design a data pipeline to ingest and process user interaction data from Netflix’s streaming service?

The interviewer at Netflix will assess your ability to design efficient data pipelines for ingesting and processing large volumes of data, a critical aspect of data engineering.

How to Answer

Discuss the components of the data pipeline (e.g., data ingestion, processing, storage) and the technologies you would use (e.g., Apache Kafka for streaming, Apache Spark for processing). Emphasize scalability, fault tolerance, and data consistency in your design.

Example

“To design a data pipeline for ingesting and processing user interaction data at Netflix, I would employ a combination of Apache Kafka for real-time streaming ingestion and Apache Spark for distributed processing. Kafka would handle data ingestion from various sources, ensuring fault tolerance and scalability, while Spark would process the data in parallel to extract insights. Additionally, I would incorporate data validation and error handling mechanisms to ensure data quality throughout the pipeline.”

21. How would you collect and aggregate data for multimedia information, specifically when it’s unstructured data from videos?

When designing an ETL pipeline for a model that uses videos as input, your ability to efficiently collect and aggregate multimedia data is crucial.

How to Answer

Discuss the three levels of video data aggregation: primary metadata collection and indexing, user-generated content tagging, and binary-level collection. Highlight the importance of leveraging machine learning techniques for automated content analysis to enhance scalability and accuracy.

Example

“To design an ETL pipeline for processing video data, I would start with primary metadata collection and indexing, gathering essential information like author, location, format, and date of capture to create an efficient index. Next, I would implement user-generated content tagging, initially utilizing manual tagging but scaling it with machine learning models for text mining and automated tagging to enrich the dataset. For the binary-level collection, I would use advanced algorithms to analyze and aggregate detailed binary data, such as colors, brightness levels, and audio features, despite the higher resource costs. Additionally, I would incorporate automated content analysis techniques like image recognition and object detection to further analyze visual content, along with NLP models for transcribing and analyzing audio.”

22. Write an SQL query to create a metric to recommend pages for each user based on recommendations from their friend’s liked pages.

When designing a recommender system, your ability to leverage SQL to create effective recommendations based on user interactions is essential.

How to Answer

Discuss the process of associating users with their friends’ liked pages, filtering out pages the user already likes, and calculating a recommendation score. Highlight the use of SQL joins and aggregations to achieve this.

Example

“To design a recommender system using SQL, I would start by associating each user with the pages liked by their friends. This can be achieved through an initial join between the friends and page_likes tables, creating a comprehensive view of user interactions. Next, I would filter out pages that the user already likes by performing a LEFT JOIN with the page_likes table and selecting rows where the user ID is NULL. Finally, I would group the results by user and page IDs, counting the distinct number of friends who liked each page. This approach provides a recommendation score based on the number of friends’ likes, helping to identify the most recommendable pages for each user.”

Data engineering candidates are thoroughly vetted at Netflix before onboarding. Here are a few tips on how to prepare for the role:

Master Data Analysis and Machine Learning Techniques

Ensure you have a strong foundation in statistical analysis, machine learning algorithms, and data manipulation techniques. This includes proficiency in programming languages such as Python or R, along with libraries like Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch.

Consider our Data Analytics, Product Metrics, Python, and Machine Learning Courses to prepare for the interview better.

Understand Recommendation Systems

Netflix heavily relies on recommendation systems to personalize content for users. Study recommendation algorithms like collaborative filtering, content-based filtering, and hybrid models. Familiarize yourself with frameworks like TensorFlow or PyTorch for building and deploying recommendation models.

Practice Interview Questions

We have comprehensive Data Engineering Questions that you’ll find resourceful and effective in preparing for the role. Religiously learn the answers to and solve more technical and behavioral interview questions for Data Engineers.

Emphasize Communication Skills

Data Engineers at Netflix work in cross-functional teams. Effective communication of complex technical concepts is also critical for the interview. Our Mock Interviews should help you shake away the initial awkwardness and give you a confidence boost.

Moreover, solidify your preparedness with our Data Engineering Crash Course and Interview Preparation Guide.

How much do Netflix Data Engineers make in a year?

The average base salary for Netflix Data Engineers hovers around $286K with total average compensation clocking $453K. Experienced Data Engineers, however, can command a base salary of $600K, with some of them earning $646K as total compensation.

Find out more about Data Engineer Salaries here.

Does Interview Query cover other companies aside from Netflix?

You can opt for IBM, Meta, Microsoft, and more. But to make it easier for you, you can try searching through our Company Interview Guides to see which companies best suit you.

Does Interview Query have job postings for the Netflix Data Engineer role?

For the latest official openings, head straight to our Data Engineering Jobs Board. However, don’t neglect general job boards and the Netflix Career Page.

Better check out our general Data Engineer Interview Questions Guide if you’re looking for more resources.

Also, we offer a comprehensive Netflix Interview Guide featuring interview tips and resources tailored for various job positions, such as Data Analyst, Machine Learning Engineer, and Software Engineer.

Practice Python questions, case studies, and SQL problems to solidify your claim over the Data Engineer role at Netflix.

To excel in your Netflix data engineering interview questions, prioritize your understanding of algorithms and database technologies, particularly SQL. It’s essential to have well-prepared experience narratives for the behavioral interview component.

Best of luck with your preparations!