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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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Students who chose to uplevel with IK got placed at
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
Sai Marapa Reddy
SWE, Machine Learning
Safir Merchant
SWE, Machine Learning
Best suited for
Current and former Data Engineers
Data/Business Analysts, BI Engineers, Database Admins, and Data Architects
Software Engineers working with data processing frameworks
Why choose this course?
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 session
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.*
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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
Learn more
Omkar Deshpande
Head of Curriculum
15+ years experience
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Jessica Griffin
Senior Technical Resourcer
9+ years experience
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Nick Camilleri
Head of Career Skills Devp. and Coaching
10+ years experience
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A typical week at Interview Kickstart
Data Engineering Course details and curriculum
Data structures and Algorithms
5 weeks
5 live classes
1
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
2
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
3
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
4
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
5
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
3 weeks
3 live classes
1
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
2
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
3
Stream Processing Systems
- Case Studies: on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube
Data Engineering
4 weeks
4 live classes
1
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
2
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
3
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
4
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
3 weeks
3 live classes
1
Interview Strategy and Success
2
Behavioral Interview Prep
3
Offers and Negotiation
Support Period
6 months
1
15 mock interviews
2
Take classes you missed/retake classes/tests
3
1:1 technical/career coaching
4
Interview strategy and salary negotiation support
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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
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Real-time progress update
11 programming languages
Get upto 15 mock interviews withhiring 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 - Data Engineering
Detailed personalized feedback
Identify and work on your improvement areas
Transparent, non-anonymous interviews
Get the most realistic experience possible
More about mock interviews
Career impact
Our engineers land high-paying and rewarding offers from the biggest tech companies, including Facebook, 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.
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.
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.
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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 Data Engineering careers at top tech companies.
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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.
1
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.
2
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
3
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
4
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
1
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.
2
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.
3
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.
1
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
Learn more about What Does a Data Engineer Do?
2
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.
FAQs on Data Engineering Interview Course
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