Introduction 

The market for trained data engineers is expected to rise rapidly. No wonder that’s the case; no matter what your corporation does, you need a strong system to both store and access the data of your enterprise to succeed in today’s competitive climate, and you need it from the very beginning. What are the skills required for a data engineer, What is the data engineer learning path, and What are data engineer responsibilities?

  1. Who is a Data Engineer?
  2. What are the Data Engineer responsibilities?
  3. What are the skills required for a data engineer?

1) Who is a Data Engineer?

Data engineering is an aspect of computer technology, a general concept that covers many areas of data-work-related expertise. At its heart, data science is all about having data to produce valuable and usable information for the study. To have utility for deep learning, data stream analysis, market intelligence, or some other form of analytics, the data may be further applied.

In particular, while computer science and data scientists are concerned with data discovery, seeking insights into it, and developing algorithms for machine learning, data engineering cares about making these algorithms operate on a processing infrastructure and generally constructing data pipelines. Therefore, a technology engineer is an engineering position within a data science team or any computer-related activity that involves the development and maintenance of a data platform’s technical infrastructure.

2) What are the Data Engineer responsibilities?

Data engineers are responsible for developing and managing the architecture for analytics that makes virtually any other feature in the field of data. They are responsible for design development, construction, maintenance, and testing, such as databases and large-scale computing systems. As part of this, it is also the duty of data engineering to establish data set processes used in the simulation, mining, acquisition, and verification.

3) What are the skills required for a data engineer?

Data engineer skills:

  • Skills for Engineering. Java (Hadoop, Apache Hive) and Scalia write most data analysis/big data tools and frameworks (Kafka, Apache Spark). Because of their success and syntactic clarification, Python along with Rlang are commonly used in data projects. Among data engineers, high-performance languages such as C/C# and Golang are also common, particularly for training and implementing ML models.
    • Context on software architecture
    • Java -Java
    • Scala Scala
    • From Python
    • R
    • C / C #
    • Golang-golang
  • Expertise associated with data. Data engineering will cooperate closely with data scientists. The foundations for working with data systems are a good understanding of data processing, algorithms, and data transformation techniques. The development of ETL (data acquisition, transformation, and loading), storage, and analytical instruments will be the responsibility of data engineers.  To take part in big data initiatives that use dedicated instruments such as Kafka or Hadoop, more technical experience is needed. 
    • Clear knowledge of principles in data science
    • Data research skills
    • Experience Hands-on with ETL software
    • Knowledge of BI tools
  • Warehouse/database. In most cases, computer engineers use advanced software in order to design and build data storage. There are relational databases in most instances, so SQL is the key thing for DB/queries that any data engineer should know. The main tools, then, are:
    • SQL/noSQL/noSQLL
    • Redshift by Amazon
    • Panoply Panoply
    • About Oracle
    • Talend Talend
    • Informatica Informática
    • Hive of Apache

Conclusion 

At present, computer engineering is one of the fastest-growing fields of technology. Computer engineers appreciate high career satisfaction, numerous innovative opportunities, and an ability to work with technology that is continuously changing. Springboard also provides a robust Bootcamp for data engineering.

To understand key network infrastructure aspects, including the design, development, and operation of Working with the ETL platform, modular data pipelines, and studying main data engineering techniques such as MapReduce, Apache Hadoop, and Spark, you can work with a one-on-one tutor. In career interviews, you can also complete two capstone assignments based on real-world computer engineering topics that you will highlight.

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 

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