Data Engineering Interview Course

Nail Data Engineering interviews at FAANG and Tier-1 Tech Companies
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Course designed and taught by instructors from FAANG & Tier-1 Tech Companies

Adrián Fernández

Engineering Manager
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Suhas Satish

Senior Engineering Manager
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Qiuping Xu.

Principal Data Scientist
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Data Engineering Course Curriculum

This is what you'll learn in our Data Engineering career path!

  • 15 Mock Interviews
  • 6-Month Support Period

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Data Engineering course and curriculum

Data structures and Algorithms
5 weeks
5 live classes
1

Online Processing Systems

Common Scalable Concepts like DBs, Cache, Messaging Queue, etc., and Common Design Problems
2

Batch Processing Systems

Batch Processing Concepts in-depth and Common Design Problems for FAANG+ interviews
3

Stream Processing Systems

  • Case Studies: on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube
Design real-time data-intensive applications like Google Maps, Netflix, etc.
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

Common Scalable Concepts like DBs, Cache, Messaging Queue, etc., and Common Design Problems
2

Batch Processing Systems

Batch Processing Concepts in-depth and Common Design Problems for FAANG+ interviews
3

Stream Processing Systems

  • Case Studies: on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube
Design real-time data-intensive applications like Google Maps, Netflix, etc.
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

Online Processing Systems

Common Scalable Concepts like DBs, Cache, Messaging Queue, etc., and Common Design Problems
2

Batch Processing Systems

Batch Processing Concepts in-depth and Common Design Problems for FAANG+ interviews
3

Stream Processing Systems

  • Case Studies: on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube
Design real-time data-intensive applications like Google Maps, Netflix, etc.
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

Online Processing Systems

Common Scalable Concepts like DBs, Cache, Messaging Queue, etc., and Common Design Problems
2

Batch Processing Systems

Batch Processing Concepts in-depth and Common Design Problems for FAANG+ interviews
3

Stream Processing Systems

  • Case Studies: on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube
Design real-time data-intensive applications like Google Maps, Netflix, etc.
1

Interview Preparation

Interview Questions
Placement assistance
Behavioral Coaching
2

Resume & LinkedIn Masterclass

3

Salary Negotiation Masterclass

Support Period
6 months
1

Online Processing Systems

Common Scalable Concepts like DBs, Cache, Messaging Queue, etc., and Common Design Problems
2

Batch Processing Systems

Batch Processing Concepts in-depth and Common Design Problems for FAANG+ interviews
3

Stream Processing Systems

  • Case Studies: on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube
Design real-time data-intensive applications like Google Maps, Netflix, etc.
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


Next webinar starts in

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Best Suited for

  • Current or Former Data Scientists
  • Software Engineers working on ML Models

Data Engineering Interview Process at Tier-1 Companies

We prepare you for all stages of a typical data engineering interview process at FAANG and Tier-1 companies

1 round of writing SQL queries

  • 5-6 problems on a dataset (understanding the trade-offs between joins, equivalent queries, and so on)

1 round based on Python, SQL, and Big Data Frameworks

  • Writing MapReduce equivalent for SQL/Spark queries
  • Programming questions on dictionary manipulations
  • Working knowledge of Hive, Spark, and other NoSQL databases

2-3 rounds on core Data Engineering concepts

  • Data modeling problem followed by related SQL questions 
  • Designing an ETL system for a given use case
  • Understanding the trade-off between tools (applicable to senior roles)
  • Optimizing and fine-tuning

1 behavioral interview round

  • Questions related to your job experience 
  • Discussions on past projects
  • Open-ended questions to gauge if you're a "good fit"
Top companies love hiring our candidates
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Top companies love hiring our candidates!

10K+

Experienced engineers enrolled

7

Years of successful training in Silicon Valley

18

Highest number of offers received by an alum

5

Avg years of experience of our alumni

What our students say

Akshay Lodha
Data Engineer
Offers from
Google_logo

"The way the instructors taught was awesome, the career coaching and the mock interview sections were also really helpful. Interview Kickstart helped me a lot in orienting myself and getting into the rhythm., and eventually transition from Goldman Sachs to Facebook."

Rupesh Dabbir
Offers from
Google_logo

Interview Kickstart (IK) provides you a solid platform to not only strengthen your algorithm and interview game, I've had the pleasure of meeting some of the best/brightest minds in the industry (Faculty and students included). It was a humble experience, to say the least.

Sujay Ghosh
Offers from
Google_logo

"Interview Kickstart Helps People Transition into FAANG Companies. I Got Into Amazon."

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