Top 13 data engineer interview questions (and tips)

Essential data engineering skills#

There are a few basic skills you’ll need to master before applying to a data engineering position.

Programming languages#

First, you’ll need to know how to program. Set aside some time to practice going over algorithms and data structures.

One of the leading programming languages used by data engineers is Python because it provides a plethora of useful libraries to facilitate data engineering.

Key libraries used by data engineers include:

• matplotlib: Used for data visualizations
• pandas: Used for data manipulation and visualization
• numPy: Provides several mathematics and statistics functions
• sklearn: Used for machine learning
• pyspark: Used to handle Big Data (ETL + Hadoop)

As a data engineer, you must know what data structures and algorithms are most suitable for different situations.

Understanding the advantages and disadvantages when it comes to different methods of organizing and transforming data is essential for strategic decision-making.

Data structures to know:

• Lists
• Arrays
• Hash tables
• Hash maps
• Stacks
• Queues
• Graphs
• Trees
• Heaps

Algorithms to know:

• Linear regression
  • Least-squares algorithm
  • Lasso shooting for sparse solution
  • Polynomial regression
  • General feature transformations
• Linear discriminants
  • Support vector machine (SVM)
  • Kernels and infinite-dimensional feature maps
• Logistic regression
• Ensemble learning
  • Decision tree (CART)
  • Random forests
  • Adaptive boosting
  • Gradient boosting
• Generative learning
  • Naive Bayes classifier
  • Markov models
• K-nearest neighbors
• Unsupervised learning
  • K-means clustering
  • Spectral clustering
  • Principal component analysis
• Artificial neural networks (ANN)
  • Convolutional neural networks
  • Recurrent neural networks

SQL and NoSQL#

Next, you’ll need a deep understanding of SQL for your interviews.

Knowing SQL can help you work in popular relational database management systems like MySQL (open-source), Microsoft SQL Server, and Oracle Database.

These days, most data is distributed over the cloud. Examples of distributed databases include MongoDB, DynamoDB, BaseX, Ignite, Hazelcast, and Coherence. These non-relational databases are called NoSQL databases.

Instead of SQL, you can manipulate data from NoSQL databases using Object-Relational Mapping (ORM). We strongly recommend brushing up on ORM for your data engineering interviews.

NoSQL databases can be further classified into the following:

• Graph databases
• Column-oriented databases
• Document-oriented databases
• Key-value databases

Data analysis#

Data engineers should have the technical skills to extract, represent, and analyze data using efficient data structures and statistical modeling. Cultivating a familiarity with the dependencies of different data attributes will enable you to design better target models. Learning these dependencies can be accomplished by using descriptive statistics to some extent.

In addition, data needs to be standardized and prepared using data preprocessing techniques to optimize for better performance. For example, real data consists of a mixture of several data types including text, dates, numbers, etc. In contrast, a machine learning model will expect all data to be numeric. Data preprocessing includes encoding the data into numeric form by preserving the information in the data.

Mathematical foundations#

Finally, a data engineer must have a strong understanding of the different branches of mathematics. Mathematical foundations are essential for anyone who wishes to understand and manipulate data as a science.

The key branches of mathematics for a data engineer are:

• Discrete mathematics
• Probability and statistics
• Linear algebra
• Calculus

Interview process#

The hiring process at major companies like Amazon, Microsoft, Google, and Netflix typically consists of multiple rounds of behavioral and technical interviews. Writing Python for these interviews can be helpful, but you can generally use whatever programming language you are most comfortable in (like Java or C++).

The interview process varies from company to company but you can expect most interviews to follow a format similar to the one outlined below:

1. Prescreening: A recruiter contacts you to schedule a short phone call to go over your resume and complete a technical challenge.
2. Phone interview: The recruiter contacts you to schedule a phone interview with a senior engineer or engineering manager.
3. On-site or virtual interviews: After the phone interview, you will be invited to participate in several rounds of interviews with hiring managers and team engineers.
4. Lunch interview: There is sometimes a more casual “interview” that takes place when your interviewers take you out to lunch.
5. HR interview: This is the final interview where the hiring manager goes over anything not covered in the on-site or virtual interviews. At this point, an offer may be extended, and you’ll have the opportunity to discuss compensation.

In total, the hiring process may take anywhere from 1 to 2 months to complete from start to finish. We recommend spending 3 months preparing for your interview.

More resources for interview prep:

• Definitive Interview Prep Roadmap: A free 12-week self-study guide for the technical interview.
• Interview guides by company: These guides cover the different interview stages at specific companies in greater detail.
• Grokking the Behavioral Interview

13 data engineer interview questions#

Although this isn’t an exhaustive list, you can generally expect to encounter questions similar to the examples below. Be prepared to write Python scripts, describe and compare algorithms, and solve math problems.