Big data is considered a progressively developing field which relies on the performance of million employees. According to the statistics portal Statista, today, over 4.4 million of IT jobs are available worldwide. 1.9 million of jobs are located in the USA. With the increasing importance of big data, many new professions started popping up lately, and data engineer, data architect, and data scientist are among them. Each of these professionals makes an important contribution to the development of big data.
Big Data Engineer
Big data engineer is a professional who is responsible for preparation of the big data infrastructure. These specialists are software engineers that integrate, build, design and manage data from different sources. Then, data engineer writes complex queries and ensures that they work smoothly and may be accessed easily. Big data engineer`s goal is to optimize the performance of big data ecosystem in their company. They also create the big data warehouse used for analysis and reporting by data scientists. Being mainly focused on architecture and design, they are not supposed to know analytics or machine learning for big data.
Big Data Architect
Big Data Architect is a professional who builds BD architecture and deals with service management. This specialist is also referred to as an advanced big data engineer. These people usually have a holistic vision of the company`s architecture and have a deep understanding of the latest databases. Big data architect must exhibit a solid knowledge in data structures, database architecture, Spark, Hadoop, MapReduce, Cassandra, Kafka, Storm, and other big data technologies and networks.
Big Data Scientist
Who is Big Data Scientist?
Big Data Scientist (also called data science engineer) is someone who turns raw data into the insight. They apply machine learning, analytic approaches, and statistics to find solutions for various business problems. Their key objective is to turn the massive volumes of big data into valuable insights. Although data science engineer is not a completely new profession, it carries out an advanced data analysis which is driven by computer science and machine learning. These people must have strong analytical and programming skills and exhibit an ability to create new algorithms and process big data. They are also supposed to be experts in domain knowledge. Big data scientists interpret and deliver the results of research by using visualization techniques, developing apps, or examining information about the solutions to the business problems. Data scientists have strong problem-solving skills and deep understanding of both new and traditional methods of data analysis. They use this knowledge to discover patterns in big data or create statistical models. For instance, they use their skills to make recommendation engine or predict the stock market. For big data analysis, the big data scientists should gain insight into various tools and techniques in data mining, machine learning, big data infrastructure, and statistics. They are expected to work with datasets of different shapes and sizes and run their algorithms both efficiently and effectively. Learn more about one more type of data science engineer – Hadoop data scientist in our previous post!
The Categories of Big Data Scientist
Big data scientists are divided into three main categories: senior data scientist, middle data scientists, and junior data scientist. Despite educational background and experience, categorization is also based on the level of autonomy that data scientist exhibits. It is logical that senior data scientists are the people whose performance does not require constant checking on the part of the employers.
The Value of Big Data Engineer, Big Data Architect, and Big Data Scientist
It is obvious that big data engineer, big data architect, and big data scientists are the parts of a single whole. Their roles cannot be underestimated. Neither could they perform effectively without each other. Big data engineer creates an infrastructure that is further analyzed by big data architect. Big data scientist seems to be the leading figure in the world of big data with a mission of turning raw data into a meaningful insight. All three professionals are interdependent, meaning that with some new skills acquired, they may carry out more functions. Tim Abraham specifies that there is a sort of overlap between these professions. All these professionals make their own contribution to big data processing and analysis. They also develop business models to generate companies` success.
Ukraine and Big Data Science
In Ukraine, big data science is a popular field. If we check the job searching website, we will see that big data scientist is a widely popular vacancy. The employers require their candidates to have a broad knowledge of machine learning. Working experience is also worth much. Employers seek to hire workers with at least three years of relevant experience. This hiring tendency can be explained by the fact that big data boom is currently taking place worldwide, and Ukraine is luckily not an exception.
We see that big data needs the involvement of many professionals such as big data scientist, big data architect, and big data engineer. The role of these professionals is equally important to big data processing and analysis. Nonetheless, the big data scientist is given special emphasis. In Ukraine, employers seek to hire big data specialists with 3-6 years of experience. The demand for these professionals is high, and we hope that this is just the beginning.