THE FIVE JOB ROLES IN THE DATA INDUSTRY

When industries grow and develop, the tech industry particularly, so does research, technology, and productivity. The growing changes in the tech industry introduces the need for new specializations in different bodies. If there is such a need for new specialists in a field, then there is a need to educate and train new professionals to handle the complex and demanding tasks in order to introduce new products, grow companies, to stay ahead and avoid the danger of lagging behind technologically.

The data industry is growing rapidly. Data is now a valuable commodity for many aspiring companies to maintain a competitive edge. These companies have large databases and data storage facilities that house enormous quantities of data that will be useful for research and other profitable purposes. From business analytics to statistical modelling, to artificial intelligence, the potentials are now almost endless for these new and talented professionals.

Positions like “data scientist”, “data analyst”, “BI analyst,” and “data engineer”, now proliferate job boards. The problem with this growth is that many companies that want to onboard these roles do not always understand the requirements or tools used by the professionals they seek to employ. It gets funny as some of them do not know the right names to give these roles. Names like “big data specialist”, “AI engineer”, and “decision scientist” appear from time to time at job boards and articles. This is not surprising because the increasing confusion implies that these roles are now developing into subordinate roles and branches in various circles. The question now is what are the major roles in the data industry? What are the requirements, functions and levels in these roles?

The foundation for these roles lies in statistical methods for software engineering in IT, and not necessarily in names that originate somewhere on the internet. It is the most influential organizations that standardize the new roles that will become the professions of the future. Below are five important positions that are shaping the data industry in IT.

Data Analyst

The data analyst is considered the legacy position amongst data jobs, and an indispensable stepping stone for the higher-level roles. Despite its long history, it is still an entry-level or junior role in many job boards. In fact, it is the lowest tier role among all the other roles because the main tools are still the same, although they have been upgraded over the years. The main job of the data analyst in the office is to build the analytical dashboards that will be presented to the audiences at meetings. These dashboards display the elegant visualizations and KPIs that will display the summaries and answers to questions asked by the answers. The tools used to make these presentations include business intelligence applications like Tableau and Microsoft Power BI, spreadsheet applications like MS Excel, LibreOffice, WPS Office, and Mobisystems Office Suite. Not only do these apps make dashboards by themselves, they are primarily used for analyzing and aggregating datasets called spreadsheets before converting them into charts.

Data analysts also query and extract data from databases all the time. A data analyst who knows about databases and how to write SQL commands and queries has a greater advantage over those who only know BI and Excel but lack knowledge of database management systems. Data analysts expect to earn around $80, 000 ~ $110, 000 a year in the US. The competition for the data analyst positions these days has become more intense such that knowledge of Python and Pandas is becoming a requirement on job boards. This means that in the coming years, being a data analyst won’t be an entry-level position. Positions like business analyst, research analyst, financial analyst, and BI specialist are actually just data analysts focusing on a specific departmental task in a company.

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Data scientist

“Data scientist” is a word that people who follow tech trends have been hearing about in recent years without knowing the meaning. Some confuse it with data analyst, data engineer and machine learning engineer positions. Others say a data scientist is a data analyst transitioning to machine learning and software development. Indeed, it looks like an intersection of the set of job roles. Generally, data science refers to a field of scientific research projects where statisticians, and other experts discover actionable insights in data using programming and other data analytics tools. Data science performs better in academia and departments of research and publicity.

To become a data scientist requires one to be skilled in statistics, programming in Python language and the Jupyter framework. He must also be familiar with databases and tools used by data analysts. Data science in terms of experience is quite distant from entry-level positions. It can take three to five years of training and hard work before a person can establish his position as a data scientist. Only the best and brightest would succeed to get to top tier software engineer roles in the industry. Depending on the level of experience, data scientists can earn between $120, 000 and $150, 000 in the US.

Database administrator

A database administrator is a personnel in an IT company or the section of the IT department of a given company whose job is to manage relational and non-relational databases. Just as an operations manager manages company operations, a database administrator manages database operations and the tables and query files therein.? The read-write operations, transactions, security, backup and access privileges are in the hands of the administrator. The database administrator grants or revokes access to any personnel who wants to read or write to a database table.

To become a database administrator, one must have superb SQL skills and a good history of keeping records in administrative roles such as operations manager or secretary. Imagine the difficult task of looking after a couple of databases, and all the tables in each one. Database administrators are required to work on enterprise database software such as Oracle, Microsoft SSMS, and MySQL servers with good efficiency. The average database administrator can earn between $95, 000 and $140, 000 per year in the US.

Machine Learning Engineer

Sometimes informally called “AI engineer” or senior-level “data scientist”. The function of a machine learning engineer is to build various machine learning models for AI applications that will run on the cloud. The ML engineer also builds intelligent programs to automate a variety of systems. They work with teams of data analysts, cloud engineers, database administrators, and DevOps engineers for the specific goal of bringing a machine learning or deep learning solution to the project where it fits.

Machine learning engineers are also full-stack software developers because only they know how to design AI app applications such that users can enjoy the intelligent interface and experience these apps portray. At the back end, they query, clean and transform huge quantities of data to train into new models. It takes much more years of training and hard work to become a machine learning engineer in a big industry than for new data scientists. Machine learning engineers can earn up to and sometimes above $200, 000 per year.

Data Engineer

The data engineer is another senior-level position that cooperates with the machine learning engineer in database access and utilization of cloud software. However, they sometimes compete with machine learning engineers in job listings. The data engineer builds and configures ETL or ELT pipelines from across data sources to the consumers. Data engineers are analogous to civil engineers who design and construct processing plants, storage tanks, and pipelines to extract oil, gas, or water, processes them in distillation machines and transports the refined products to huge storage tanks.

From those tanks, a network of interconnected pipelines and infrastructure will further transport them to the final consumer. Data engineers likewise transform data, store them in intermediary data warehouses or data lakes, where they will be segmented before being distributed to the many subscribers who need them at that moment. Data engineers work with machine learning engineers, database administrators, and other IT personnel to design these systems. That is why data engineers are sometimes called “data architects”. Like machine learning engineers, data engineers can earn up to $240, 000 in the US.

How these roles interoperate

The most basic role is the Data analyst role. This is the indispensable data role because everyone who wants to work in data must first know the nuts and bolts, and intricacies of data analysis. As a result, data analysts easily gain access to databases handled by other roles.

The Data scientist is like a bridge that holds all the roles. Not only that, data scientists make the knowledge of data science projects attractive and insightful both within the corporate or academic environment but also to the general public. Without the progress of data scientists, there won’t be employable data engineers or machine learning engineers in the coming years.

The Data engineer and Machine learning engineer are the most advanced roles in the tier. They both have a good deal of tech knowledge and skills that enable them to work with software developers, web developers, cloud and DevOps engineers. The only difference between both positions is that data engineers work on ETL/ELT pipelines and big data infrastructure and design while machine learning focuses on AI and intelligent automation using machine learning and data analytics methods.

There is however no better or best role mentioned here. Each specialization has its function and challenges that come with it. The trainee who aspires to become a full-fledged professional must climb the ladder of learning, and must dedicate a period of hard work and practice to actualize his or her aim.