14 Data Engineer Interview Questions and How to Answer Them
Any job interview can be stressful. Data engineer positions can be very competitive in the IT sector. These professions draw a lot of interest because they are in great demand, pay well, and have promising long-term job development.
Be proud of how far you've gone in your data engineering journey as you get ready for an upcoming interview.
It takes longer than you anticipated to find a job in big data because of the intense competition; some job seekers report applying for hundreds of positions before they are even called in for an interview.
Once done so, you must distinctly explain why and how you used specific data techniques and algorithms in a prior project to get the job.
Here is a list of the 14 Data Engineer Interview Questions and How to Answer Them.
Describe yourself to me.
This interview question, which is actually about how you feel about data engineering, can come out as generic and open-ended due to how frequently it is asked. Focus your response on how you plan to become a data engineer. What drew you to this line of work or sector? How did you learn the technical talents you possess?
What role does a data engineer play on a team or business?
Recruiters want to know that you understand what a data engineer does to answer this question. How do they behave? What function do they fulfil within the team? It would be best if you listed the typical duties and team members a data engineer collaborates with. You might wish to mention that if you've previously worked with data engineers as a data scientist or analyst.
What is a data pipeline?
A data pipeline is a series of processes that move data from one place to another. It is a way to automate the movement and transformation of data from various sources, such as databases, file systems, or real-time streams, to a destination, such as a data warehouse, database, or analytics platform.
What is ETL?
ETL stands for Extract, Transform, Load. It is a process used to move data from one system to another, often involving extracting data from various sources, transforming that data into a consistent format or structure, and loading the transformed data into a destination system.
What is a data lake?
A data lake is a central repository that allows you to store structured and unstructured data at any scale. It is a way to keep large volumes of data in a single, centralised location, where various tools and systems can access and analyse it.
What is a data warehouse?
A data warehouse is a centralised repository of structured data for reporting and analysis. It is designed to support efficient querying and analysis of large volumes of data and to support the needs of business intelligence and data analytics applications.
What is a batch process?
A batch process is a series of automated tasks executed without user interaction. These tasks are typically run on a schedule, such as daily or weekly, and are often used for functions that do not require real-time processing, such as data transformation or loading.
What is a real-time process?
A real-time process is a process that is executed as soon as the data becomes available without any delay. Real-time processes are used for tasks that require immediate processing, such as streaming data analysis or event-driven applications.
What is a data mart?
A data mart is a subset of a data warehouse focused on a specific business line or department. It is a way to provide tailored data and analytics capabilities to particular groups within an organisation.
What is a schema on reading vs schema on write?
In a schema on a read system, the data structure is not enforced when loaded into the system. Instead, the structure is defined when the data is queried or read from the system. In a schema on the writing system, the data's structure is enforced when loaded into the system, and data that does not conform to the defined structure is rejected.
What is a dimension table?
In a data warehouse, a dimension table is a table that contains descriptive attributes, such as product names, customer names, and location names. Dimension tables are typically used in conjunction with fact tables to provide context for the measures contained in the fact tables.
What is a fact table?
In a data warehouse, a fact table is a table that contains measures, such as sales amounts, quantities, and costs. Fact tables are typically used in conjunction with dimension tables to provide context for the measures contained in the fact tables.
What is normalisation?
Normalisation is the process of organising a database in a way that reduces redundancy and dependency. It is a way to structure a database to minimise data redundancy and improve data integrity.
What is denormalisation?
Denormalisation is intentionally adding redundancy to a database to improve performance. It is often used in data warehouses, where the goal is to improve query performance by denormalising the data model and adding pre-computed results or summaries.