Land high-paying Data Engineer jobs by cracking tough technical interviews at FAANG+ companies. Enroll in the most comprehensive Data Engineering Interview Course designed and taught by FAANG+ engineers to help you ace your interview.
Crucial topics for FAANG+ interviews
Sample questions and problems asked at FAANG+ interviews
Videos of FAANG+ experts teaching Data Engineer topics
Derive business insights for a food delivery app by writing SQL queries
Comprehensive coverage of topics from intermediate-level concepts such as Case Statements and subqueries to advanced SQL functions such as joins and analytical functions
Application of window functions as lead, lag functions to evaluate day-over-day insight on business performance
Use rank and dense rank functions to understand merchants’ reach in the market
Complex SQL problems on customer-merchant pairwise dependence using a variety of functions and operators
Deep dive into joins, their type, and comparison of left join vs. right join vs. outer join vs. broadcast join
Thematic coverage of frequently asked interview problems through template problems
A step-by-step guide to what you can expect in an interview and how to tackle them in a time-constrained environment
Design Data Warehouse tables for Uber or a similar ride-sharing platform
Coming up with a conceptual and logical model, define data granularity
Define the fact and dimension tables with high-level attributes
Best practices on how to choose keys and constraints for the entities
Discussion on how to normalize tables
How to handle cases of Slowly Changing Dimensions
Thematic discussion on interview problems from Meta, Amazon, Twitter, and Uber
Learn how to decide your data warehouse schema: Star vs. Snowflake schema design
A step-by-step guide to approaching atypical interview questions
ETL and Pipeline Design
Create a data pipeline for near-real-time ingestion of Netflix clickstream/playback data. Design for ad-hoc monitoring of certain metrics
Comprehensive coverage of different stages of design: Upstream, ETL environment, and downstream requirements
Gain interview perspective on essential ETL design techniques such as handling data ingestion, different file formats, data granularity, landing and storage levels, and reporting metrics
Detailed outline of performance parameters depending on data granularity, volume, velocity, accepted latency, etc.
A top-down approach to building a high-level architecture: Identify available technology at each stage
How often do you update your data in DW?
Pipeline has been fine for 6 months; now, certain marketplaces have more aggressively incoming data. How would you handle that? What changes would you make to your design if new data is more unstructured?
Discussion on trivial but important questions: What is being monitored? Does everything go into one monitoring dashboard?
What would the architecture look like for the ML platform that uses this data?
Discussion on the role of DE in large-scale, multi-faceted systems, what you can expect in an interview, and how to tackle them in a time-constrained environment
Design a data platform for a gaming company. Understand data-driven approach in deciding business metrics
Breaking down high-level components of Data Platform design: Ingestion, Warehousing, Transformation, Catalog and Governance, Privacy & Access, and Visualization
Structured discussion on how to define data flow and come up with a DAG
Learn how to design high-performance platforms at scale
How do you implement a production-ready design using Kafka and Spark? Orchestrate your pipeline using Airflow (or alternate services)
How do you define your success metrics? How do you gauge the relevance of your data? At what frequency do we capture and process it?
How do we ensure data backup, and at what scale?
Discussion of optimization techniques at scale like partitioning, distributed platform, cloud services, etc.
An insightful discussion on Product Sense, working with different aspects of data engineering systems, what you can expect in an interview, and how to tackle them in a time-constrained environment
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Our engineers land high-paying and rewarding offers from the biggest tech companies, including Facebook, Google, Microsoft, Apple, Amazon, Tesla, and Netflix.
Senior Data Engineer
Interview Kickstart provides a nice, structured way of interview preparation for experienced professionals. I would like to thank each and every mentor from IK for their time and effort. I got multiple FAANG offers from TOP companies that includes Amazon and Facebook. I have joined Facebook as a Senior Data Engineer.
Lead Data Engineer
I really like the courses. For many working professionals, going through examples and different perspectives are the most valuable. I started interviewing for FB in May, then took IK to study for this specific company. IK was great because its structure helped me really understand each problem in my interview.
Data Engineering & Analytics
The experience with IK was phenomenal, it was totally worth it. After so many years I was interviewing and IK helped me a lot in orienting myself and to get into the rhythm. Had a transition from Goldman Sachs to Facebook. IK mentors guided me and told me not to worry about the preparation part and to focus on upskilling myself. That really made a huge difference.
Data Engineering Consultant
The program has been really fantastic. It has given me insights of what I need to study and how to approach problems. The patterns that are taught in IK is something that I really enjoy and have not been taught in my many years of experience in the industry and studying for interviews. The approach is really fantastic. The learning experience was also great. The big support system is also what I really admire about IK.
How to enroll for the Data Engineering Interview Course?
Learn more about Interview Kickstart and the Data Engineering course by joining the free webinar hosted by Ryan Valles, co-founder of Interview Kickstart.
The interview process for Data Engineers typically evaluates your knowledge of core SQL, Big Data, coding, and behavioral concepts. To tackle Data Engineer interview questions asked at FAANG+ interviews, it’s extremely important to know what to expect at these interviews.
Here’s what the Data Engineering interview process at top companies looks like.
1 round of writing SQL queries - 5-6 problems on a dataset (understanding the trade-offs between joins, equivalent queries, and so on) and related Data Engineer interview questions.
1 round based on Python, SQL, and Big Data Frameworks - Writing MapReduce equivalent for SQL/Spark queries, solving programming questions on dictionary manipulations. Data engineering interview questions in this round also test your working knowledge of Hive, Spark, and other NoSQL databases.
2-3 rounds on core Data Engineering concepts - These rounds involve solving Data Modeling and SQL problems, designing an ETL system for a given use case, explaining trade-offs between tools (applicable to senior roles), data optimization, and fine-tuning.
1 behavioral interview round - In this round, you can expect questions related to your job experience, discussions on past projects, and open-ended questions to gauge if you're a "good fit.”
At top companies, the interview process for Data Engineers is pretty standard. To give you a clearer idea, let’s see what the interview process looks like at top companies.
Amazon Data Engineer Interview Process
Amazon is known to have one of the most challenging interview processes for Data Engineers. The interview essentially evaluates your knowledge of core data structures, algorithms, ML concepts, SQL concepts, and data-oriented design concepts. The interview process at Amazon also has a special Bar Raiser round where you’re evaluated by a specially trained Bar Raiser to see if you’re the right cultural fit at the company.
The interview process consists of:
1 coding round
1-2 design rounds, 1-2 SQL rounds
1 domain-specific round (ETL, Data Modeling, and Data Visualization)
1 behavioral/leadership round and the Bar Raiser round
The Facebook Data Engineer interview process is similar to the process at Amazon but doesn’t include the Bar Raiser round. You can expect:
Technical phone screen: Questions on core Data Structures (coding) and SQL Queries
2 ETL rounds: Questions on design problems for real-time and batch processing systems
1 Data Modeling round
An on-site interview: 1-2 coding rounds, 1 SQL round, 1 round on domain-specific tools and concepts, and 1 leadership/behavioral interview
Google Data Engineer Interview Process
Google also has an intense interview process for Data Engineers. At the Google interview, you can expect:
Technical phone screen: 1-2 coding and SQL rounds
1-2 ETL rounds
1 Data Modeling and Data Visualization round
An on-site interview: 1-2 coding rounds, design round (data-engineering specific), behavioral and leadership round
Apple Data Engineer Interview Process
Apple has one of the most challenging interview processes for Data Engineers. At the Apple Data Engineering interview, you can expect:
Technical phone screen: Coding, ETL, and Data Modeling rounds
1-2 SQL rounds
An on-site interview: 1-2 coding rounds, design round (data engineering specific), behavioral and leadership round
Data Engineer Interview Questions
If you’ve just begun your Data Engineering interview preparation, it is important to know the type of Data Engineering interview questions to expect. The better your ability to tackle tough Data Engineer interview questions, the better your chances of landing dream Data Engineer jobs at FAANG+ companies.
Data Engineer interview questions are typically around coding, Big Data, and Data Engineering-related concepts, SQL queries, and behavioral aspects.
Before we look at some sample Data Engineer interview questions, let’s first take a quick glance at the important concepts to prepare from an interview perspective.
Below are the concepts you should definitely cover for your Data Engineering interview.
Algorithms and Data Structures
Product Sense, Metric Design
Automation tools like Airflow
Data Pipeline Design
DB Performance Tuning
Data Engineering Interview Questions on Coding
Given an integer array arr of size n, find all magic triplets in it. A magic triplet is a group of three numbers whose sum is zero.
Given an array of integers, find any non-empty subarray whose elements sum up to zero.
Given an unsorted set of numbers from 1 to N with exactly two of them missing, find those two missing numbers.
For an array of integers and unique values, write a program code to decipher if the sum of any two integers in the array is equal to a given value.
Data Engineering Interview Questions on SQL Queries
You’re given a dataset with information on users who’ve purchased a list of products. Design a dashboard to highlight specific aspects of user behavior.
You’re given a dataset with the number of users visiting an e-commerce site and purchasing a long list of products. Find the top-performing product in the last one hour.
Create DDL (table and foreign keys) for several tables in a provided ERD.
Create a real-time dashboard to return the number of views for a popular video posted online. Also, find how many users didn’t watch the entire length of the video.
You’re given a raw table with information. Use ETL design to create a clear table with neatly distributed information using SQL.
Generic Data Engineering Interview Questions
How would you handle duplicate data points in an SQL query?
For an expected increase in data volume, what steps would you take to add more capacity to the data processing architecture?
For a given array of integers of length n spanning 0 to n with one missing, you have to write a function missing_number that returns the missing number in the array.
For a given list of integers, write a program to find the index where the sum of the left half of the list equals the right half. Return -1 if there is no index satisfying the condition.
When would you use the NumPy library vs. pandas?
Don’t forget to check company-specific Data Engineering interview questions:
Opting for a career in Data Engineering and landing high-paying Data Engineer jobs from FAANG+ companies can yield several benefits, including getting to work on high-impact projects and rewarding salaries that directly equate to a better lifestyle.
As such, knowing the main responsibilities of Data Engineers is important when applying for Data Engineering jobs. To give you a clear idea, we’ve listed the main roles and responsibilities associated with Data Engineering roles.
Data Engineering Job Requirements: Roles and Responsibilities
Data Engineers play an important role in making informed and complex decisions based on available data. They’re involved with processing huge chunks of organized and unorganized data to drive business processes and decision-making. Let’s look at the typical role of Data Engineers at top companies.
Manage large quantities of data and prepare complex data sets
Leverage raw data to make business decisions
Work closely with application developers to design data-driven applications and processes
Build and maintain database architectures
Design and develop tools for automation and processes
Work closely with software engineers, product managers, and data scientists on projects that involve leveraging datasets
Use SQL and OOP programming to build predictive algorithms
Perform data modeling, data visualization, and carry out ETL design
Career Roadmap of a Data Engineer in a FAANG+ Company
In a FAANG+ company, the career progression of a Data Engineering role is :
Data Engineer 1 → Data Engineer 2 → Senior Data Engineer → Staff Data Engineer → Sr. Staff Data Engineer → Principal Data Engineer
Interview rounds for every level have questions on coding, design, domain, and soft skills, but in varying degrees. As you advance to senior roles, you’ll get fewer coding rounds and more design/domain rounds. But as far as Data Engineer interview prep is concerned, one has to prepare for all four topics, and we cover all of these in the course.
Data Engineer Salary at Top Companies
Data Engineer salaries in the US range from $77,350 to $221,342 per year, depending on the company. The average Data Engineer salary in the US is $123,000 per year. This includes a base pay average of $112,493 and an additional pay average of $10,507.
In this section, we’ll look at Data Engineer salaries at top companies in the US.
Facebook Data Engineer Salary
The average Data Engineer salary at Facebook is $119,747 per year. This includes a base pay average of $1,09,514 and an additional pay average of $10,030.
Apple is known to offer some of the industry’s highest salaries for Data Engineers. The average Data Engineer salary at Apple is $173,657 per year. This includes a base pay average of $168,055, an average stock bonus of $39,655, and a cash bonus of $14,489.
Technical Program Manager at Apple
Average compensation by level
Netflix Data Engineer Salary
Netflix is also known to offer some of the highest salaries to Data Engineers in the US. The average Data Engineer salary at Netflix is $127,770 per year.