What are the Career Paths that a Data Engineer Can Take?


Data engineering is one of the fastest-growing jobs in the field. The need for data engineers has increased by 40% over the years, while data science interviews grew just by 10%. With the need for competent data engineers growing, more professionals and graduates are considering careers in data engineering. The role of a data engineer is to build a system that collects, processes, and stores data. Data engineer might build a data pipeline that can collect raw data from various source such as CRM and sales data and transforms it into clean, reliable information which delivers data to end-users like data scientists and analysts. Data engineer plays a vital role for businesses, and they are responsible for creating and maintaining the data infrastructure which analysts and data scientists use to identify insights and drive business value. 

For building a career in data engineering, one must avail of the course and take data engineer training. Enroll on a different platform and gain knowledge about the different data engineering courses. Data engineering is a field of computer science that involves the collection and analysis of data. The demand for the data engineering profession has increased. It is important to know that a data science degree isn’t training for a data engineering career. Data engineers work in job profiles like data warehouse engineers, data platform engineers, data infrastructure engineers, analytics engineers, data architects, and DevOps engineers. 

Data engineering is a profitable, secure, and demanding career, and individuals who are looking for a complete step-by-step data engineering carer path must check the insights. Before checking on the career path, let us know who is a data engineer. A data engineer is a person who is responsible for managing data workflows, pipelines, and ETL processes. Data engineering is associated with data with delivery, storage, and processing. He is responsible for collecting, storing, and pre-processing the data for data scientists and data analysis. 

A data engineer is responsible for preparing data for analytics or operational users. They are also responsible for building data pipelines for pulling valuable information together from various sources. Data engineers mainly focus on making data secure and accessible for data scientists so they can analyze the same. Data engineering involves the use of different tools and ways to improve the quality, reliability, and efficiency of data. 

Let us check the roles and responsibilities of data engineering:

  • Convert erroneous data into a usable form for further analysis
  • Create a large data warehouse using ETL
  • Develop, test, and even maintain architecture
  • Develop dataset processes
  • Deploy machine learning and statistical methods

Data engineer responsibilities:

  • Partner with leadership, engineer, program manager, and data scientist for knowing the data needs
  • Design, build and even launch the efficient and reliable data pipeline for moving data across a number of platforms, including a data warehouse. 
  • Communicate via multiple mediums, i.e., presentation, dashboard, company-wide dataset, bots, etc. 
  • Use data and analytics experience for identifying and even addressing gaps in their existing logging.
  • Leverage data and business principles for solving large scale web and data infrastructure issues
  • Create data expertise and own data quality for the areas

For becoming a data engineer, you must require an undergraduate degree in computer science, IT, Software engineering, Math, or a business-related field. This is a required qualification for a data engineer. Sometimes not only does a degree serves the purpose, but some required skills are even essential for becoming a data engineer. 

Let us take an insight into the essential in-demand skills:

  • Programming language: candidate must have knowledge of the programming language, which is required for a data engineer. There are different data engineering-specific programming languages such as Python, Java in Scala. The demand for Python is high as compared to Java and Scala. 
  • In-depth database knowledge: for a data engineer, one must have to deal with data during all working hours. For this, one must have in-depth knowledge of database language and tools. Knowledge of SQL is even beneficial. 
  • Knowledge of big data tools: in every company, data is increasing at an alarming rate. For processing a huge amount of data, one must be familiar with big data tools. Some companies do mention a point named knowledge of big data tools as a compulsory for data engineers post. Interested candidates must know about big data tools such as Hadoop and MapReduce, Apache Spark, Apache Hive, Kafka, Apache Pig, and Sqoop.
  • Data warehousing and ETL tools: a data engineer is required to perform ETL operations. Data warehousing is needed for managing a large sum of data. Knowledge of ETL tools such as Informatica and Talend and Data warehousing solutions like Redshift or Panoply is valuable. Informatica and Talend are well-known tools with ETL architecture. 
  • Data engineering cloud platform: there are different cloud or on-premise-based platforms that are available, i.e., Google cloud platform, AWS, Azure, and Apprenda. Mastering every tool is not essential; even a single tool knowledge is beneficial. 
  • Familiar with operating system: data engineers must know about the ins and outs of infrastructure components, i.e., virtual machines, network application services, etc. 
  • Machine learning: knowledge of ML is essential for the domain. A data engineer must have a basic understanding of machine learning algorithms.
  • Data visualization tools: this is a representation for finding with the help of graphs, charts, and visual formats. Tableau and PowerBI are important popular data visualization tools. 

How to become a data engineer?

  • Start with a programming language: you must have a better understanding of programming language and software engineering concepts. The industry revolves around technologies such as python and scala. 
  • Get in-depth knowledge of SQL and NoSQL: this is a demanding skill for data engineers, and NoSQL is even required to deal with unstructured data. 
  • Learn big data tools: master Python and SQL and then learn bg data tools, so knowledge of Big data tools is required. 
  • Understand and learn ETL tools: data engineers must have to perform ETL operations, and one must become familiar with ETL tools. 
  • Study cloud computing: more and more app workloads are moving to a different cloud platform. This is why the data science and engineering community has a good understanding of clouds. 


The data engineer career path is a fulfilling one that offers a chance to design and build data applications. This is the most in-demand job in technology right now. 


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