Data engineering interviews can feel unpredictable and you don’t quite know what to expect! In this article, I’ll be going over the main types of interviews to expect as you go through out your data engineer journey.
The main interviews are:
SQL interview (free detailed video, blog post)
This interview occurred in 100% of my interview loops
Data Structures and Algorithms Interview (free detailed video, blog post)
This interview occurred in 100% of my interviews loops
Behavioral Interview (free detailed video)
This interview occurred in 100% of my interview loops
Data Modeling Interview (free detailed video, blog post)
This interview has occurred in 90% of my interview loops
Data Architecture Interview (free detailed video, blog post)
This interview has occurred in 75% of my interview loops
The SQL Interview (100% of the time!)
This is the most common interview! A data engineer without SQL skills doesn’t really count as a data engineer! Remember to consume the more detailed options here: free detailed video, blog post
Here are the most important strategies to remember:
Understanding the Problem Before Coding:
Spend the first few minutes thoroughly understanding the problem. Clarify any ambiguities by asking questions. This helps prevent time-consuming mistakes and demonstrates strong communication skills.
Efficient Use of SQL Constructs:
Be proficient in using different SQL constructs such as JOINs, window functions, and common table expressions (CTEs). Understand when and how to use each construct for optimal performance and clarity.
Optimizing Queries:
Focus on writing efficient queries. Minimize the number of table scans by combining conditions and using appropriate indexing. For instance, prefer using CASE statements within a single SELECT for multiple counts over multiple UNION statements.
Handling Common Interview Questions:
Prepare for common types of SQL questions:
Simple WHERE condition with GROUP BY
JOIN operations (LEFT, RIGHT, INNER, FULL OUTER)
Window functions (RANK, DENSE_RANK, ROW_NUMBER)
CTEs and subqueries
Self-joins and optimizations
Communicating Your Thought Process:
Verbally explain your thought process while solving problems. This helps the interviewer understand your approach and provides an opportunity to correct any misunderstandings early.
Using EXPLAIN and Query Plans:
Familiarize yourself with the EXPLAIN keyword to analyze query plans. Being able to discuss query optimization and explain the execution plan can showcase your depth of knowledge.
Handling Follow-Up Questions:
Be prepared for follow-up questions on your solutions. Understand the trade-offs of different approaches, such as the impact of indexes on read-write performance and the benefits of partitioning.
Data Structures and Algorithms Interview (100% of the time!)
Some companies think software engineers and data engineers are the same role! So you’ll probably need to “grind Leetcode” a bit to pass data engineering interviews! Remember there is more detailed content here: free detailed video, blog post
The key things you need to remember are:
Challenges and Common Experiences in DSA Interviews:
Data structures and algorithms (DSA) interviews can be highly stressful, with a significant amount of luck involved. So don’t take it personally if you fail this interview every once in a while!
Successful interviews often feature good rapport with the interviewer, clear communication, and a strong grasp of fundamentals.
Effective Preparation Strategies:
Structured practice is essential; "grinding Leetcode" should be done with a clear plan, committing to a manageable number of problems daily.
On the day of the interview, ensure good sleep, do a short physical activity to reduce anxiety, and use a less verbose coding language like Python
Focus on understanding and applying Big O notation for both time and space complexity.
Day of Interview Tips and Differences in DSA Interviews for Data Engineering vs. Software Engineering:
Treat the interviewer like a person and aim to make them laugh to ease the serious atmosphere.
Recognize keyword cues in problems to map them to appropriate data structures (e.g., "balanced" means stack, "ordinal" means queue).
Data engineering DSA interviews tend to be less difficult than software engineering ones, often involving medium-level Leetcode questions, and focus more on time complexity than space complexity.
Common problem types include stacks, queues, maps, trees, and recursion, with emphasis on understanding time complexity.
The Behavioral Interview (100% of the time)
Behavioral interviews are the easiest interview to pass so long as you have good stories that give your interviewer FOMO about hiring you! Remember there’s more detailed information here: free detailed video
The key points to remember:
Use the STAR Method
Have stories that are interesting and show you’re great to work with!
Have a story about how you received critical feedback and what you did to change
Have a story about when things failed and how you pivoted
Have a story about a big win you achieved and what you did to get there
Use good soft skills and be personable
Being likable is one of the most important things to do in this interview
Ask good follow up questions!
Show that you’re interested in the role and you did your research about the company!
The Data Modeling Interview (90% of the time!)
You’ll get this interview in all roles except ones that are very software engineering focused! Remember there are more detailed resources here: free detailed video, blog post
Here are the main points to remember:
Key Concepts to Master:
Dimensional Data Modeling: Understanding cumulative vs. daily dimensions, slowly-changing dimension types, and the Kimball data modeling method.
Fact Data Modeling: Knowledge of normalized vs. denormalized facts and the “one big table” methodology.
Aggregate Data Modeling: Creating daily and cumulative metrics, using OLAP cubes for rapid analytics, and enabling efficient slicing and dicing of data by different dimensions (e.g., time, product, location).
Interview Strategies:
You should be comfortable producing diagrams and schemas to illustrate the data models
Discuss different grains and aggregates, but avoid getting too bogged down in technical details.
Focus on trade-offs in data modeling choices and demonstrate an understanding of business metrics.
Engage in a back-and-forth dialogue with the interviewer to clarify requirements and iterate on solutions
The Data Architecture Interview (75% of the time!)
The data architecture interview will show up more and more often as you get more senior in your career! I was asked these at Netflix and Airbnb but not Facebook since I was a junior engineer hire at Facebook! Remember there’s more detailed information here: free detailed video, blog post
Key points to remember:
Structure
It typically involves a 60-90 minute discussion about technical tradeoffs and potential solutions, possibly including whiteboarding.
Core Concepts Tested
Understanding Tradeoffs: The interview assesses the candidate’s ability to evaluate and manage tradeoffs in data architecture.
Lambda vs. Kappa Architectures:
Lambda Architecture: More complex with separate speed and batch layers, offering low latency and correctness at the expense of complexity.
Kappa Architecture: Simpler, streaming-only approach with lower latency and simplicity, but potential issues with data quality.
Serving Layer: Choosing appropriate databases like Redis, Memcached, Druid, Cassandra, or MongoDB based on latency and data size requirements.
Data Quality Checks: Implementing data contract patterns and testing streaming pipelines for errors.
Database Choices: Understanding the CAP theorem and the tradeoffs between consistency, availability, and partition tolerance.
Conclusion
Employing these strategies helps me pass about 80% of my data engineering interviews. There are always a few unlucky interviews that most people experience in there career!
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What other things have you seen in interviews? Did I miss anything critical that you would add? Make sure to share this article with your friends who are hungry to break into data engineering!
nice content btw for DSA
Thanks for this article Zach! I also wanted to ask about materials related to data cleaning steps to be considered while designing data pipelines and how to make sure the data quality is maintained. Could you suggest any links for these as well? Thanks!