As the digital world continues to thrive, data has become one of the most important resources in the globe. Industries and organizations around the world rely on data to understand trends, metrics, performances, solve problems and improve processes. In recent years, the data ecosystem has grown rapidly all around the world, and the need for various data professionals has sky-rocketed.
According to the US Bureau of Labour Statistics, the need for data-related professionals is expected to grow by more than 31.4 percent by the year 2030.
There are a plethora of professions in the data ecosystem, a proper understanding of their various role helps in choosing a career in the booming data world.
In this article, we take a look at eight data professions, their roles, annual salary and the various tools used by these professions.
• Data Analyst: A data analyst is a person whose job is to collect, clean, model and interpret dataset in a visual form, in order to answer questions or solve problems.
They create appropriate documentation that allows stakeholders to understand trends and insights in order to make better decisions. In addition, they also design and maintain data system and database where companies store their data.
A data analyst makes use of tools such as Microsoft Excel, Google sheets, Tableau, Power BI, SQL and so many more. According to glassdoor.com, the average annual salary of a data analyst in the United States of America is $69,517.
• Data Scientist: Data scientist is a person responsible for collecting, analyzing and interpreting extremely large amount of data.
They are also responsible for exploring and examining data from multiple sources. This job involves the use of advanced analytical technology such as machine learning and predictive modeling. They are able to predict the future based on past patterns unlike a data analyst whose job is mainly to spot patterns and trends.
A data scientist also possess strong business knowledge and data visualization skill to convert insights into an understandable business story. Tools used by data scientist include; Python, Apache, Spark, SQL, etc. The average annual salary of a data scientist is $117,212.
• Machine Learning Engineer: Machine learning engineers are responsible for designing machine learning system which involves, assessing and organizing data, executing tasks and general monitoring of an organization’s data. They are specialized in transforming models created by data scientist into real code that can be used in production.
They are also technically proficient programmers who are skilled in building self running software to automate predictive models. Machine learning engineers are also skilled in data visualization for finding deeper insights from a dataset. Tools used by a data scientist include: Python, Java, etc. Machine learning engineers are often referred to as one of the highest paid professionals in the data ecosystem, with an average annual salary of $122, 686 .
• Data Architect: A data architect is responsible for defining the policies, procedures and technology to be used in collecting, organizing and storing a company’s data. They are experts who formulate an organization data strategy, including standard of data quality, the flow of data within the organization and security of the data.
They are also responsible for defining the process involved in testing and maintaining a company’s database. Tools used by data architects include; Archi, Navicut, SQL, ER/ Studio, ERBuilder, and so many more. According to Glassdoor.com, a data architect earns $118, 868 per year.
• Data Engineer: Data engineers are usually employed by industries and organization where large amounts of data are stored. They are responsible for creating and developing new ways to access and store large amounts of data.
They are also responsible for building data pipelines to bring information from different source systems. Other roles include, building systems that collect, manage and convert raw data into usable dataset for data analysts and scientist to interpret. Their most important role is making data accessible to an organization in order to carry out analysis. Data engineers make use of tools such as SQL and Python. Average annual salary earned by data engineers in the United States is $112, 493.
• Database Manager: A database manager is responsible for developing and managing a company database, they create data storage and retrieval systems, troubleshoot database issues and implement recovery procedures.
They are also responsible for implementing standardized database management procedures and overseeing the installation, security and upgrade of databases. Other roles of database manger include, supervision of the daily activities of database teams and hiring and mentoring database teams. HeidiSQL, MySQL, SQL server, , Oracle DBMS, are the most important tools used by a database manger. Average annual salary of a database manager is $75, 550.
• Business Intelligence Analyst: A business intelligence analyst uses data to help businesses navigate decisions. They are responsible for reviewing data to produce finance and market intelligence report.
A business intelligence analyst also determines a business critical priorities and requirements, define key performance indicators and helps identify trends and metrics to enable executives to make intelligent business decisions. Business intelligence tools include Power BI, Tableau, Sisense, Qlik, Looker and so many more. A business intelligence analyst earns an average annual salary of $84, 635 yearly.
• Big Data Analyst: Big Data analysis is a relatively new field in the data ecosystem. Big data refers to a large volume of data growing at an extremely high velocity. A big data analyst is responsible for reviewing, analyzing and reporting on big data stored by an organization.
A big data analyst has a similar job description as a data analyst, but they specialize in the analysis and exploration of big data. Tools used by a big data analyst include; MapReduce, Hive, Impala, Apache Spark, and so many others. According to glassdoor.com, average annual salary of a big data analyst in the United States is $89, 789.
Other data professions include Data Annotator, Data Jounalist, and so many more.
Clive Humby, a British mathematician, described data as the new oil. Hence, building a career in the data ecosystem is being part of a profession that is here to stay.