Interviewing Big Data Engineer

Data engineering involves designing and creating systems that collect, store, and analyze data at scale. A simple example: Organizations in every sector collect vast amounts of data, and to extract valuable information from this data, they need the right technology and experts (data analysts, data scientists, data engineers). The future of most industries is heavily determined by technology. So, hiring a prominent data engineer is challenging, as technological infrastructure is required to manage the machine learning process. You need to structure your interview correctly and understand the role and responsibilities of a prominent data engineer.

Roles and Responsibilities of a Data Engineer

The role and responsibilities of a data engineer vary depending on the scope and requirements of a particular project and the complexity of the infrastructure.

Role

A data engineer might be part of a small team responsible for working with data, from configuring data sources to integrating analytical tools. The role of the data engineer on such a team is to design and manage all these systems. Today’s data warehouses are much more diverse than they used to be, so there is a growing demand for professionals specializing in warehouse design and understanding big data tools and storage systems. This is another role of a data engineer.

Some engineers focus on managing a specific ecosystem level, such as pipelines. Integration tools connect the data warehouse to data sources. These tools can perform specific tasks or move data from one place to another for further transformation. In summary, data engineers are involved in creating pipelines according to business needs and developing, optimizing, and managing the infrastructure.

Responsibilities

The process of working with data can be divided into three main phases: Extract, Transform, and Load. These form the so-called ETL pipeline, or, in simple terms, a set of tasks.

Extract: To transform data into valuable information, it must first be extracted from its sources.

Transform: Raw data is challenging to analyze and useless to end users. The transformation phase involves cleaning, formatting, and structuring the dataset to make it available for analysis and reporting.

Load and save: The extracted information must be stored somewhere, like loaded into memory.

To be successful as a data engineer, you need engineering, computer science, and database skills and knowledge. Depending on the position and work experience, specialist responsibilities may include:

Designing platform architecture (in large companies, the architect is usually a separate position)
Managing, configuring, and creating integration tools, data warehouses, analytics systems, etc.
Testing/maintaining pipelines
Deploying machine learning algorithms (often performed by machine learning engineers)
Metadata management
Providing access tools

Interview Structure of a Data Engineer

Your data engineer interview can be divided into three rounds:

Round 1: Technical Screening (1 hour)

Objective: Gauge the candidate’s basics in big data concepts, tools, and programming languages.

Round 2: Hands-on Coding and Problem-solving (1.5 hours)

Objective: Assess the individual’s skill in developing, optimizing, and troubleshooting data processing pipelines with big data frameworks.

Round 3: System Design and Architecture (1 hour)

Objective: Evaluate the candidate’s ability to design scalable extensive data systems while ensuring performance optimization and troubleshooting problems.

Interview question of Data Engineer

What are the 3 Vs (or 4 Vs) of Big Data? Give examples of each.
What are the different types of data – structured, semi-structured, unstructured?
When would you use Hadoop instead of Spark? In what cases can you choose it?
Explain the role of a Data Lake and a Data Warehouse.
What is data serialization and why does it matter in big data?
What are some challenges associated with data pipelines?
How do you handle poor-quality issues in your data pipeline?
Define what data lineage means and what its significance is.
Any known conventional design patterns for the Database Warehouse, such as Star Schema or Snowflake Schema.
Have you ever had any experience with SQL and/or related technologies?
Create a data pipeline that extracts information from a specific source (e.g., social media API), modifies it, and loads it into the data warehouse.
Describe how you would optimize a slowly performing data processing job.
Create an error scenario in a data pipeline. How can one find out the issue?
Create a Spark SQL query that will perform a specific form of data analysis (whole performance requirements).
Put together scalable big data architecture for coping with a high influx of real-time data.
How do you achieve high availability and disaster recovery in a big data system?
Distinguish between various alternatives for storing information, i.e. HDFS and S3.
What is your experience using cloud-based infrastructures for big data like AWS EMR or GCP Dataflow?
Suppose there are some performance problems in the cluster; how would you monitor and troubleshoot them?

These questions represent only some of the possible queries; depending on your needs and level of responsibility, they can be different. At any rate, evaluating a candidate’s problem-solving abilities, ability to work as part of a team, or interpersonal skills is also essential.

Conclusion

This guide aims to give recommendations on how to interview big data engineers. I have talked about different rounds of interviews, the skills that you should look for in each round, and have provided sample questions that can be used during such an interview. Besides technical abilities, I recommend evaluating soft skills like problem-solving and teamwork.