Data Engineering Interview Course

Nail Your Next Data Engineering Interview

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Students enrolled: 343

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.

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Data Engineering Course Overview

Start Learning

Get all the information about the course and pricing in our live webinar with Q&A.

Students who chose to uplevel with IK got placed at

Sai Marapa Reddy

SWE, Machine Learning

Safir Merchant

SWE, Machine Learning

Mike Kane

Lead Data Engineer, Analytics

Akshay Lodha

Data Engineering & Analytics

Jaime Lichauco

Database Engineer

Anju Mercian

Data Engineering Consultant

Alokkumar Roy

Data Engineer

Sayan Banerjee

Data Scientist II

Siva Karthik Gade

Software Development Engineer

21,000+

Tech professionals trained

$1.28M

Highest offer received by an IK alum

53%

Average salary hike received by alums

Best suited for

Interview Kickstart ProjectUp – Key Outcomes

Program designed by FAANG+ leads

Covering data structures, algorithms, interview-relevant topics, and career coaching

Individualized teaching and 1:1 help

Technical coaching, homework assistance, solutions discussion, and individual sessions

Mock interviews with Silicon Valley engineers

Live interview practice in real-life simulated environments with FAANG and top-tier interviewers

Personalized feedback

Constructive, structured, and actionable insights for improved interview performance

Career skills development

Resume building, LinkedIn profile optimization, personal branding, and live behavioral workshops

50% Money-Back Guarantee*

If you do well in our course but still don't land a domain-relevant job within the post-program support period, we'll refund 50% of the tuition you paid for the course.*

Next webinar starts in

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Meet your instructors

Our highly experienced instructors are active hiring managers and employees at FAANG+ companies and know exactly what it takes to ace tech and managerial interviews.

Dorando Morrison

Application Manager

9+ years experience

FAANG Company

A typical week at Interview Kickstart

This is how we make your journey structured and organized. Our learners spend 10-12 hours each week on this course.

Thu

Get Foundational content

Get high-quality videos and course material for the upcoming week

Covers fundamentals, interview-relevant topics, and case studies

Assignment review session

Solve questions and case studies based on the assignment shared with you

Detailed feedback on your approach and interview-focused solutions

Sun

Attend online live sessions

Attend 4-hour sessions hosted by Data Engineers at FAANG+ companies

Discuss open-ended interview questions and problem-solving strategies

Get pro tips to solve challenging system design problems

Mon-Wed

Practice problems & case studies

Apply the concepts taught in live sessions to solve assignment questions

In class, discuss case studies' solutions and practice answering frameworks

Live doubt-solving with FAANG+ Data Engineering instructors

Every day

1:1 access to instructors

Personalized coaching from FAANG+ DE instructors

Individualized and detailed attention to your questions

Solution walkthroughs

Data Engineering Course details and curriculum

Data structures and Algorithms

Sorting

Introduction to Sorting
Basics of Asymptotic Analysis and Worst Case & Average Case Analysis
Different Sorting Algorithms and their comparison
Algorithm paradigms like Divide & Conquer, Decrease & Conquer, Transform & Conquer
Presorting
Extensions of Merge Sort, Quick Sort, Heap Sort
Common sorting-related coding interview problems

Recursion

Recursion as a Lazy Manager’s Strategy
Recursive Mathematical Functions
Combinatorial Enumeration
Backtracking
Exhaustive Enumeration & General Template
Common recursion- and backtracking-related coding interview problems

Trees

Dictionaries & Sets, Hash Tables
Modeling data as Binary Trees and Binary Search Tree and performing different operations over them
Tree Traversals and Constructions
BFS Coding Patterns
DFS Coding Patterns
Tree Construction from its traversals
Common trees-related coding interview problems

Graphs

Overview of Graphs
Problem definition of the 7 Bridges of Konigsberg and its connection with Graph theory
What is a graph, and when do you model a problem as a Graph?
How to store a Graph in memory (Adjacency Lists, Adjacency Matrices, Adjacency Maps)
Graphs traversal: BFS and DFS, BFS Tree, DFS stack-based implementation
A general template to solve any problems modeled as Graphs
Graphs in Interviews
Common graphs-related coding interview problems

Dynamic Programming

Dynamic Programming Introduction
Modeling problems as recursive mathematical functions
Detecting overlapping subproblems
Top-down Memorization
Bottom-up Tabulation
Optimizing Bottom-up Tabulation
Common DP-related coding interview problems

System Design

Online Processing Systems

The client-server model of Online processing
Top-down steps for system design interview
Depth and breadth analysis
Cryptographic hash function
Network Protocols, Web Server, Hash Index
Scaling
Performance Metrics of a Scalable System
SLOs and SLAs
Proxy: Reverse and Forward
Load balancing
CAP Theorem
Content Distribution Networks
Cache
Sharding
Consistent Hashing
Storage
Case Studies: URL Shortener, Instagram, Uber, Twitter, Messaging/Chat Services

Batch Processing Systems

Inverted Index
External Sort Merge
K-way External Sort-Merge
Distributed File System
Map-reduce Framework
Distributed Sorting
Case Studies: Search Engine, Graph Processor, Typeahead Suggestions, Recommendation Systems

Stream Processing Systems

Data Engineering

SQL Programming

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

Data Modeling

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
Follow-up questions:
- 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

Data Platforms

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

Career Coaching

Interview Strategy and Success

Behavioral Interview Prep

Offers and Negotiation

Support Period

15 mock interviews

Take classes you missed/retake classes/tests

1:1 technical/career coaching

Interview strategy and salary negotiation support

Next webinar starts in

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Practice and track progress on UpLevel

UpLevel will be your all-in-one learning platform to get you FAANG-ready, with 10,000+ interview questions, timed tests, videos, mock interviews suite, and more.

Mock interviews suite

On-demand timed tests

In-browser online judge

10,000 interview questions

100,000 hours of video explanations

Class schedules & activity alerts

Real-time progress update

11 programming languages

Get upto 15 mock interviews with

hiring managers

What makes our mock Interviews the best:

Hiring managers from Tier-1 companies like Google & Apple

Interview with the best. No one will prepare you better!

Domain-specific interviews

Practice for your target domain - Machine Learning

Detailed personalized feedback

Identify and work on your improvement areas

Transparent, non-anonymous interviews

Get the most realistic experience possible

More about mock interviews

1. Flexible schedule

Pick timings convenient to you

4. Technical and behavioral interviews

Uplevel your technical and behavioral interview skills

2. Remote interview experience

Mirrors the current format of remote interviews

5. Level-specific interviews

Because an L4 at Google can be quite different from an E7 at Meta

3. Feedback documentation

All the feedback you’ve ever wanted, recorded and documented

6. Interviewer of your choice

Choose based on company and/or domain

Career impact

Our engineers land high-paying and rewarding offers from the biggest tech companies, including Meta, Google, Microsoft, Apple, Amazon, Tesla, and Netflix.

Neha Garg

Joining Interview Kickstart for data engineering was a game-changer! The comprehensive curriculum, real-world scenarios, and expert guidance significantly enhanced my technical skills. The mock interviews boosted my confidence, preparing me well for job interviews.
I highly recommend Interview Kickstart's data engineering program. The knowledgeable instructors and top-notch materials, along with the emphasis on problem-solving, contributed to my success in landing a data engineering role. I'm grateful for the valuable skills and insights gained through this program.

Varun Reddy

Works at:

My experience with Interview Kickstart has been amazing. The technical and conceptual expertise that they provide really helps in building strong fundamentals. They cover all the aspects of interview preparation viz algo & ds, system design, technical skills, behavioral and interview skills. The mock interviews provide you with real time interview experience. Absolutely recommend this !

Veera Pandyala

Works at:

Best world class Computer Science and ML training from Industry Experts
I have learned a lot about Data Structures and Algorithms before, and now I am studying a course on Machine Learning, which is really good. It would have been difficult to understand these subjects without this help.
The team provides regular question and answer sessions, practice tests, interviews, and doubt-clearing sessions. They also keep us updated with the latest news. These classes are valuable and beneficial.
I highly recommend others to take advantage of these opportunities.

Jodie Zhou

Works at:

IK has great materials and resources to help you find a job. It's a self-paced, well planned learning system if you're serious enough to commit decent amount of time in the process. I would recommend IK if you want to land a better job through a systematic approach!

Kunal Seth

Works at:

IK has very high quality content, they are experts in the field of interviewing. The support has been phenomenal. From technical sessions to customer support everyone is helping you out to succeed in the program. I have been guided and motivated by IK support on multiple occasions from study plan to strategy in finishing the material. I would highly recommend IK. It's investment worth making for your career.

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.

Next webinar starts in

00 DAYS

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A Free Guide to Kickstart Your Data Engineering Career at FAANG+

From the interview process and career path to interview questions and salary details — learn everything you need to know about Back-End Engineering careers at top tech companies.

Interview Strategy and Success

Interview Questions

Career Path

Salary and Levels at FAANG

Frequently Asked Questions

Data Engineer Interview Process Outline

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

Learn some tips on How to Prepare for the Amazon Data Engineer Interview.

Facebook Data Engineer Interview Process

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
Spark, Kafka
Automation tools like Airflow
SQL
Data Pipeline Design
DB Performance Tuning
Data Modeling

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:

Data Engineering Career

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

Perform data modeling, data visualization, and carry out ETL design

Learn more about What Does a Data Engineer Do?

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.

Amazon Data Engineer Salary

The average Data Engineer salary at Amazon is $130,725 per year. This includes an average stock bonus of $22,309 and a cash bonus of $27,505.

Apple Data Engineer Salary

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.

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.

Google Data Engineer Salary

Salaries for Data Engineers at Google can range from $64,000 to $185,00 per year. The average Google Data Engineer salary is $124,188 per year.

You can learn more about more related topics on our companies age.

FAQs on Data Engineering Interview Course

What coding concepts are important for Data Engineering interviews at FAANG+ companies?

The important coding topics for Data Engineering interviews are:

Sorting Algorithms
Arrays, Strings, and Linked Lists
Recursion
Hash Tables
Graphs and Trees
Dynamic Programming

What does Interview Kickstart’s Data Engineering Course Cover?

Interview Kickstart’s Data Engineering course covers coding, systems design, Data Engineering concepts, Big Data technologies, SQL, Data Modeling, Visualization, and behavioral interviews.

What is the average Data Engineer salary in the USA?

How long does Interview Kickstart’s Data Engineering course last?

Who is Interview Kickstart’s Data Engineering course best suited for?

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.

Nail Your Next Data Engineering Interview

Register for webinar

It's Free

Next webinar starts in

00

DAYS

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HR

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SEC

Data Engineering Course Overview

Start Learning

Students who chose to uplevel with IK got placed at

21,000+

Tech professionals trained

$1.28M

Highest offer received by an IK alum

53%

Average salary hike received by alums

Best suited for

Interview Kickstart ProjectUp – Key Outcomes

Program designed by FAANG+ leads

Individualized teaching and 1:1 help

Mock interviews with Silicon Valley engineers

Personalized feedback

Career skills development

50% Money-Back Guarantee*

Register for webinar

It's Free

Next webinar starts in

00

DAYS

:

00

HR

:

00

MINS

:

00

SEC

Meet your instructors

Dorando Morrison

A typical week at Interview Kickstart

Thu

Sun

Mon-Wed

Every day

Data Engineering Course details and curriculum

Sorting

Recursion

Trees

Graphs

Dynamic Programming

Online Processing Systems

Batch Processing Systems

Stream Processing Systems

SQL Programming

Data Modeling

ETL and Pipeline Design

Data Platforms

Interview Strategy and Success

Behavioral Interview Prep

Offers and Negotiation

15 mock interviews

Take classes you missed/retake classes/tests

1:1 technical/career coaching

Interview strategy and salary negotiation support

Register for webinar

It's Free

Next webinar starts in

00

DAYS

:

00