Data Engineering for Data Scientist – Why is it important?

Data engineering refers to the process involved in building and designing complex systems for the collection, storage, and analysis of data. Organizations function by collecting and utilizing a vast amount of data. This data is converted into a format usable by data scientists. 

Larger firms have specialist data engineers for data engineering. Still, many smaller ones delegate this task to their data scientists, making it mandatory for them to possess a good number of the skillsets characteristic of data engineers. 

Data engineers are responsible for processing raw data. They work behind the scenes, building infrastructure to fine-tune data sets. They are also responsible for processing data on a large scale and the maintenance and monitoring of systems. They have the tasks of acquiring datasets that align with business needs, building, testing, and maintaining the database’s pipeline architectures. Listed below are some of the data engineering skillsets:

  • Coding: Data engineers must (at least on a fundamental level) comprehend data structures and algorithms. A mastery of coding languages such as SQL, Python, NoSQL, Scala, Java, and R are vital to this role. They must be learned and practiced till the coder develops a level of proficiency.
  • Distributed systems: These include software engineering and architect skills (e.g. Hadoop, Hive, etc.).
  • Relational and Non-relational databases: Sufficient knowledge of databases and how they work is vital to an aspiring data engineer.
  • ETL systems: Extract, transform and load refers to relocating data from databases into a data warehouse. A data engineer must use ETL tools such as Alooma, Xplenty, Talend, etc.
  • Scripting and Automation: A data engineer must write scripts that make it possible to automate repetitive tasks due to the amount of information collected.
  • Machine learning: This is present in most areas of data science, and as such, data scientists should have no problem with the basic concepts of machine learning.
  • Analytics: This is typically the realm of data scientists but is required of data engineers. It involves the grasp of various mathematical, statistical, and probabilistic principles needed for data manipulation.
  • Big data tools: The evolution of technology means a data engineer must harness big data tools like MongoDB, Hadoop, Kafka, and scala.
  • Cloud computing: The increasing reliance of companies on cloud storage and computing means these would have to be understood by data engineers and data scientists intending to be capable in that field.
  • Data security: Many companies have specialized teams for data security, but where these aren’t available, a data scientist with the required skills can fill in, helping to prevent theft and loss of data.

Having considered who a data engineer is and the skillsets required for data engineering, a review of data scientists and their skillset is necessary to connect the parallels between these two related yet different fields.

Data Scientist

Data science simply refers to using that big data to identify patterns and make informed business decisions. It requires the use of machine learning algorithms and applied-math general techniques to create prediction models.

A data scientist is often capable of the following:  

  • Data Manipulation: Collecting, entering, and removing data.
  • Data Maintenance: Data verification, staging, processing, and warehousing data.
  • Data Processing: Summarization, modeling, and clustering/classifying data.
  • Communicating Results: Creating comprehensive reports based on data provided and making informed decisions based on visualized data.
  • Data Analysis: Mining texts, predictive analysis, and exploration/confirmation of data outcomes are integral functions of a data scientist.

According to Data Science Central, in the absence of a data engineer, a data scientist will spend three percent of his time building training sets, sixty percent of it cleaning and organizing data, nineteen percent of it collecting data sets, nine percent mining for data patterns, four percent refining algorithms and five percent on other functions. Cleaning and organizing data sets and managing data sets take up seventy-nine percent of the time. 


While this function is unarguably vital, it is not the unique function of a data scientist but that of a data engineer. The absence of a data engineer means a data scientist not capable of performing these tasks cannot do about four-fifths of his job. It is only in theoretical analysis that a data scientist’s job is clearly defined and separate from that of a data engineer. As stated earlier, except for large firms, most companies do not have data engineers and data scientists wholly separated.

In the early days of data analysis, all data scientists were expected to create the required infrastructure and data pipelines that would allow them to perform their tasks. Still, these didn’t match their skillsets of job expectations and frequently resulted in data modeling not being done appropriately. These complications caused redundancies and inconsistencies in the way data was used. This led to data projects yielding less than optimal values and their predictable failure.


With this overlap between engineering and data science roles, it would be easy to erroneously believe that a data scientist can easily double as a data engineer; however, skill level in some functions highlight fundamental differences between both fields. 

While data scientists are typically well-versed in mathematics and statistics, machine learning, and algorithm techniques, data engineers lean more towards MySQL, NoSQL, SQL, cloud and architecture technologies, etc.

In the words of data scientist Soner Yildirim, a data engineer is a data engineer, but a data scientist should be both a data scientist and a data engineer.

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