The Data engineer interview questions position is a highly technical role. It is this role that ensures that the organization is abreast with current trends and equipped with the right mix of tools and technologies for efficient operations. While the role of a data engineer is the back office, it is not isolated. The engineer works hand in hand with data scientists, data architects, and others to ensure a streamlined flow of information. For this reason, it takes Data Engineering training bootcamp, certifications, refined skills, experience, and much more to shape data engineers that will bring value into the business.
13 Data Engineer Interview Questions
The interview for the Data Engineering position is also not as direct as those of other positions. Recruiters will often ask experience or practical questions that revolve around experience with tools, technologies, and innovations to test experience level, skills, proficiency with tools, and other soft skills like communication and leadership.
Here is what you should expect from a Data engineer interview questions session
General data engineer interview questions
- What do you consider to be the main responsibilities of a data engineer?
Date engineers have several responsibilities. Overall. A data engineer is in charge of the entire data flow process right from the time it is sourced, manipulated, distributed from system to system including designing and building systems, architecture, managing the pipeline through which data flows, and integrating other technology tools used in the course of the flow of data. Some of the specific responsibilities of a data engineer include:
- Designing, developing, building, testing, and maintaining the architecture of distributed systems and databases.
- Maintaining data pipelines and managing dataflow and processing within the pipeline
- Managing ETL and data transformation activities
- Developing data-related tools and technologies like databases, systems, and warehouses
- Provide data integration and access tools
- Data and metadata management
- ML modeling
- What are some common problems that data engineers face in the course of their duty?
- Real time/ continuous data integration
- Storage of vast amounts of data, data quality
- Metadata management
- Data security
- Processors and RAM configuration
- Understanding business problems and aligning them with appropriate data-driven solutions
- Managing failures and work-related frustrations
- How is the data engineer job different from a data architect job?
The roles of the data engineer overlap in many ways with that of a data architect depending on the job setting. However, while the data architect is mainly involved with designing and building data systems and managing servers, the data engineer tests and maintains the systems built by the data architect. In many situations, the data engineer and architect will work together in the same team.
- In your opinion, what are the important requirements of a data engineer?
From my experience, a data engineer should be proficient in the following fields and languages.
- Applied math, specifically probability and linear algebra
- Trend analysis and regression
- Working knowledge of programming languages like Python
- Machine learning
- SQL databases and Hive QL
- Hadoop framework
- Trend analysis and regression
- AWS platform
NB: when answering this question, it is important to explain your experience with each technology
- What is your experience with data modeling and which data modeling tools are you familiar with?
One of the basic tasks a data engineer will handle relates to data modeling.
For the companies I have worked for, I have made it a priority to learn the specific data models that the company uses.
I have comfortably used data modeling tools like SQL database modeler and Oracle SQL modeler to build two models.
Technical Data Engineering interview questions
- What are the main differences between OLTP and OLAP?
Online transaction processing (OLTP) is a system widely used by clerks and IT professionals to manage operational data in real-time. Because of this, OLTP is customer-oriented, features a 100 MB – GB of database capacity and very fast processing speeds. It makes use of the entity-relationship (ER) model together with a database.
Online analytical processing (OLAP), on the other hand, is a database analytics tool used by data analysts and managers to manage historical data that has been accumulated from OLTP systems. OLAP features far more complex queries as it handles data aggregation and summarization, data storage, and data management tasks. It is more market-oriented as it helps make important business decisions based on information obtained from past data. OLAP uses several models including snowflake and star alongside subject-oriented databases. It features a much larger 100GB – TB of database capacity.
- Which two main types of the schema are used in data modeling?
There are two types of the schema used in data modeling. These are:
- Star schema features dimension tables with hierarchies connected to a fact table and provides faster cube processing.
- Snowflake schema which features dimension tables with snowflake style hierarchies stored in separate tables. Owing to the complex join of the table, cube processing in snowflake schema is pretty slow.
- What steps should you take to deploy Big Data solutions?
Deploying Big Data solutions takes three main steps. These are:
- Data ingestion referring to the collection of data from various sources. Data can be collected from SAP, MYSQL, or internal databases in real-time or batch tasks.
- Data storage is the process of keeping data in databases like HDFS or NoSQL as it awaits preparation or processing.
- Data processing is done through MapReduce and other frameworks to prepare it for analysis.
- What happens when a Block Scanner discovers a corrupted data block?
When the block scanner locates a corrupted data block in HDFS, the DataNode transmits this information to the NameNode. Because HDFS stores replicas of blocks, it is possible for the remaining good replicas to create a new data block in place of the corrupted one. The NameNode marks the corrupted block and then schedules for a good copy of the block to be replicated on another DataNode to restore the replication factor to its original level and the corrupted block is then deleted.
- How is a Block Scanner disabled on HDFS DataNode?
In HDFS the configuration dfs.datanode.scan.period.hours is usually set to the number of hours intervals at which the Block Scanner should run. When set to zero, the scanner will not run on the HDFS DataNode.
- Which two transmissions does the NameNode get from the DataNode?
- The block report is a compilation of the list of all HDFS data blocks hosted on the DataNode.
- Heartbeat refers to the signal transmitted between the two regularly to indicate the presence of the DataNode.
- Name the default port numbers on which the NameNode, task tracker, and job tracker run in Hadoop
- NameNode runs on 50070 port
- Task Tracker runs on 50060 port
- Job tracker runs on 50030 port
- How can data analytics and Big Data increase revenue?
There are many ways in which data analytics and Big Data can increase business revenue including:
- Data can help monitor business growth and discover when growth stalls or declines for prompt action to be taken.
- Data analytics help identify customer trends and values to build products that meet their needs.
- Analytics delivers business insight for informed decision making when forming business growth strategies.
- Data analytics can help the business to discover and explore opportunities ahead of the competition.
Data engineers will have a background in computer science, applied mathematics, and IT. It is a heavily technical role which can be boosted a great deal by skill targeted certifications. Still, hands-on experience remains a crucial criterion that recruiters will always want to assess during the interview.