Data engineering is one of the fastest-growing domains in the tech industry. Data engineers are crucial to an enterprise’s data analytics team. They primarily prepare data for analysis or the organization's operational uses. They build data pipelines to collect information from different data sources. They also oversee, optimize, manage, and monitor data extraction, storage, and distribution across the organization.
Being such as key role, the number of data engineering job openings is always on the rise.
If you are preparing for a data engineering interview, check out our interview questions page and salary negotiation ebook to get interview-ready! Also, read How to Prepare for Amazon Data Engineer Interview and Facebook Data Engineer Interview Questions for specific insights and guidance on Data Engineering interviews at FAANG companies .
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In this article, we’ll talk about what exactly the role of a data engineer is. We’ll cover:
So, What Does a Data Engineer Do? The job of data engineers is to clean and transform large data sets and develop algorithms that can make raw data more useful to the company. Several technical skills are required for this role, including a strong understanding of SQL database design and various other programming languages .
Data engineers must have a clear understanding of what insights business leaders need from large datasets.
The job of a data engineer is not limited to this. He must have a deep understanding of data retrieval optimization and developing dashboards, reports, and visualizations for the team or clients.
Difference Between a Data Analyst, Data Scientist, and a Data Engineer A Data Analyst is the one who analyses numeric data to help companies make better decisions.
Analyzing and interpreting complex data is the job of a Data Scientist . They are those who organize big data to make sense of it.
A Data Engineer prepares the data that is to be used by others in the organization, including development, construction, testing, and maintenance of data.
Data Engineer Job Responsibilities So, in summary, the main role of data engineers is to create systems that collect, manage, and transform raw data into meaningful information to be used by business analysts and data scientists. Let’s break it down further — the following are some of the common tasks performed by data engineers:
Gather and analyze large volumes of composite data to determine whether they meet functional/non-functional requirements Write algorithms to convert data into useful and meaningful info Build database pipelines Make sure the policies for data governance and security are adhered to Design data validation tools and procedures Ensure compliance by adhering to policies related to data privacy and security Create and maintain infrastructure for data optimization Improve internal processes by automating manual processes, optimizing data delivery, and redesigning infrastructure for greater scalability. Utilize SQL and big data technologies to obtain, transform, and load data from a variety of data sources Develop analytics tools that rely on the data pipeline to reveal key insights into customer acquisition and operational efficiency Provide technical support for data-related issues to the Executive, Product, Data, and Design teams Improve data systems by working with data and analytics experts Qualifications and Skills Required for Data Engineering Jobs at FAANG The qualifications and skills required to apply for a data engineering position may differ from company to company and role to role. However, the following are some of the common qualifications that recruiters at FAANG and other Tier-1 tech companies seek:
Graduate degree in Computer Science, Information Systems, Informatics, Statistics, or another quantitative field. 5+ years of experience as a Data Engineer SQL knowledge and knowledge of one general-purpose language (Python is preferred since the data pipelines in most companies use Python) Experience in building robust and high-performance data pipelines Data modeling for large-scale analytical systems Hands-on experience with data platforms Strong analytical skills related to unstructured data Robust project organizational and management abilities Experience in supporting and working across teams The following are some of the software/tools you should be familiar with:
Hadoop, Spark, Kafka. NoSQL databases, with Postgres and Cassandra Data pipeline and workflow management tools: Azkaban, Luigi, Airflow AWS cloud services: EC2, EMR, RDS, Redshift Stream-processing systems: Storm, Spark-Streaming Types of Data Engineers Data engineers can be divided into three types based on the specific skills they possess:
Analytical Skills: Engineers with analytical skills usually come from a Computer Science, Math, or Physics background. They are responsible for scaling and developing machine learning models to fit in the production environment. They often have good knowledge of Tensorflow and Keras.Builder Skills: Data engineers with good builder skills mostly focus on monitoring of resources, provisioning, pipeline, and volumetry. They have good knowledge about APIs and libraries. These professionals are backed by project management and computer systems training and are capable of tracking down and archiving terabytes of data.Developer (Coding) Skills: These data engineers are specialists in developing big data, batch, or real-time applications. They are well-versed in the architecture and standards of software as well as programming languages.What Is the Average Salary of a Data Engineer? According to Glassdoor, the average data engineer salary is $137,776 per year. The salary ranges between $110,000 to $155,000 for different levels, experience, and locations.
Here’s a breakdown of data engineer salaries based on seniority (average total salary in FAANG companies):
What Is a Data Engineer’s Career Path? The role of a data engineer is not always entry-level. Many data engineers begin their careers as software engineers or business intelligence analysts. Your career may progress into managerial roles or roles such as data architect, solution architect, and machine learning engineer.
In a FAANG+ company, the career progression would look something link :
Data Engineer 1 → Data Engineer 2 → Senior Data Engineer → Staff Data Engineer → Senior Staff Data Engineer → Principal Data Engineer
How to Crack a Data Engineer Interview A typical Data Engineer interview at FAANG+ companies includes:
1 round of writing SQL queries 1 round based on Python, SQL, and Big Data Frameworks 2-3 rounds on core Data Engineering concepts 1 behavioral interview round In addition to these, you can also expect some coding questions on data structure and algorithms. To perform well and crack these interview rounds, your prep must cover all ground. This will require extensive planning and smart execution.
If you need help with your prep, consider joining Interview Kickstart’s Data Engineering Interview Masterclass — the first-of-its-kind, domain-specific tech interview prep program designed specifically for Data Engineers. Click here
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