In this post, I will explain the data roles that exist today and in particular – who is a data engineer? What are the role definition, responsibilities, and challenges contained in it?

For the past few years, I have been working as a big-data engineer, and although it sounds like this is the current buzzword, I have come to know that many of my colleagues in the software world do not necessarily understand the content of this role.
Some will confuse it with DevOps, or data analytics, or data science. Some people will think this is the new branding for the mythological role of Database Architect (DBA).
So after finding myself explaining several times to many different people what I do, and why it’s different from the ones I mentioned earlier – I realized that there might be a few more people who would be happy to know what data engineering is all about.
But First: What Data Roles Exist Today?
To be honest, the confusion is pretty understandable – today, a lot of companies have realized the importance of data to the organization, and in a world where every basic action done is translated into data and used by someone, almost every company has a data group where the role is defined a little differently.
Sometimes the Data Group will serve as one group for the whole company, usually in a small company with a specific domain, but as the company grows that way it is likely that for every department with a specific domain there will be a dedicated data group to hold the data processes.
These are the key roles in a data group:
- Data Analyst – a data analyst’s job is to translate information into knowledge, identify trends and use the analyzed data as a strategic engine for making better data-based business decisions. Its main tools will be databases, SQL and HIVE queries, and graphical dashboards for data visualization.
- Data Scientist – solve business problems using data-driven algorithms, machine learning, often with extensive knowledge of statistics and mathematics, looking for trends and patterns in data to take the interests of the company to the next level.
- Data Engineers – Build and maintain the data infrastructure such as data pipeline, responsible for transferring the data from the different sources to one place used by the rest of the roles, prepare the data for model building by the data scientist.

Types Of Data Engineer
A data engineer not only "gets you" the data, but also allows you to access data conveniently, collecting up-to-date data at any time even in real-time.
The "Classic" Data Engineer – Data Pipeline Engineer
Most of the work is based on transferring data from different data sources to a single target, in many cases, they will mainly use ETL (ETL stands for Extract, Transform, and Load, refers to the process of extracting data from multiple sources transforming it by business needs and load it to the target database) or build and maintain such.
Data engineers of this type require a very high understanding of relational databases and SQL queries in particular.
Machine Learning Data Engineer
The main role of these will be to deploy models (developed by data scientists) to a live production environment with all that entails – building a production infrastructure that includes automation, testing, monitoring, and logs.
Machine learning engineers will be part of writing the code for training and preparing the models (data preparation and training layer in Big Data Solution), and in this case, a strong background in Python, Spark, and the Cloud environments is a must.
Main Skills For A Data Engineer
While data scientists typically have a strong background in math and statistics, data engineers will typically be software developers with several years of experience, with knowledge of cloud infrastructures and development languages like Python or Java, Scala, and so on.
Since we are in a big data world, it is usually managed in the cloud so knowledge with one of the providers will be useful – such as Google Cloud Services, Azure, or AWS.
In addition, knowledge of databases is one of the things needed for the job – understanding relational and non-relational databases, running complex queries for data fetching, and all this without affecting the data used for the production environment.
In some cases, a basic understanding of machine learning algorithms, statistical models, and various mathematical functions will be required depending on the project the engineer is working on.

Challenges As A Data Engineer
Reliability
The most important thing in the data world is the reliability of the data – no sophisticated model can help in case your data is corrupted. Because the data engineer is responsible for collecting the data, sometimes from different sources and moving them to one target, transform and manipulating them to create uniformity, and more, there is a fear that the reliability of the data will be harmed along the way.
This is the big challenge, to ensure that along the way we did not change the essence of the data, and what we received is the same as what we passed on.
To provide a high level of certainty in the data, we must take action all the way, for example:
- Data consistency – means that each variable throughout the data has a single meaning. To ensure data reliability we must verify the consistency of the schema- each record is treated the same way for a particular schema.
- Metadata repository – providing context to the data by keeping orderly metadata from where it came from and how the processing was performed.
- Data Modification Permissions – only those who are authorized to modify the data do so – people and process alike. This will ensure that no unexpected changes occur.
Scalability And Performance Analysis
Sometimes the volume and velocity of incoming data are unpredictable, and one of the challenges in the role is to build a system that knows how to easily and quickly deal with increased load.
It is important to understand that there is no magic solution for Scale, but the solution will be given according to the question – how can you handle the load? For example, if your system is a web API, the load may affect the response times so the solution should be at this level.
Reproducibility
Data is the basis for everything. So one should be prepared for cases where some of the data will be lost due to various reasons. Therefore the ability to recover efficiently and quickly, and keep the data available over time, is an important challenge for the data engineer.
Conclusion
To sum up, the confusion about who a data engineer is and what their responsibilities are is understood. It is indeed an interesting and diverse role that includes code writing as well as maintenance and establishment of cloud infrastructure, complex work with databases, and in some cases also statistics and machine learning.
Data infrastructure users trust you to provide them a system where the data is reliable, with the ability to handle a sudden load quickly without losing critical data, and the ability to recover the information in unexpected cases. The many challenges in the job add a lot of interest and an impressive learning curve – the data world is evolving rapidly and to stay aligned we must be up to date with the changes and technologies we face as a solution. Furthermore, this role comes with a lot of responsibility – data reliability for example is a real challenge which in the least "worst case" means a lot of money loss, and in other cases can have legal consequences for incorrect data, or wrong decisions that can cost people lives (e.g., sensors installed on Gas tanks and real-time leak reports, if the data is translated differently in the process a real-time disaster alert may be missed).