Technology is evolving at almost terminal velocity, with an ever-changing climate of trends currently hot around the globe. When looking at the contemporary state of job postings and vacancies, one can only imagine the plethora of positions that weren’t around all that long ago.
What jobs are seen as the norm today that barely existed five years ago? We take a look at the top five positions that have yet to reach their teenage years in the modern technological age.
Big Data Engineer
The term Big Data was first coined in the 1990s by John Mashey, referring to a large set of data that is almost impossible to manage using traditional business intelligence tools. A 2011 McKinsey Global Institute report revealed that nearly all sectors in the US economy had at least 200 terabytes of stored data per company, thus the need for specialised engineers to solve Big Data problems was conceded.
Big Data Engineers develop, maintain, test, and evaluate big data solutions, on top of building large-scale data processing systems. They’re proficient in Hadoop-based technologies such as MongoDB, MapReduce, and Cassandra, while frequently working with NoSQL databases. Open source technologies are also popular amongst Big Data engineers, including Apache Flink and Spark, used for distributed stream and batch data processing.
The importance of user experience (UX) has become such a priority that we now have dedicated designers committed to getting it right. Another term originating in the ‘90s, UX is trending in a very big way, with designers concerned about experiences created and shaped through technology, and how to bring them from sketch to prototype.
The field of UX, and thus the role of UX Designers, is still incredibly new and not completely set in stone in terms of concrete definitions and tasks, however proficiency in Adobe Photoshop, along with CSS and HTML knowledge, are usually prerequisites of the industry. Specialisations can vary between design-focused roles to more technical positions, with both aspects adhering to a multidisciplinary approach in designing digital products that keep the user at the center of the process.
DevOps: What even is it? Other than calling it the collaboration between software development and operations, it’s a movement that’s still evolving and focuses very clearly on communication, integration, and the art of iterating more often to deliver software faster. These two business units have traditionally worked separately, but once agile became a household methodology for businesses, DevOps came along to ensure that deployment was part of the development process.
DevOps Managers were initially important shoes to fill for large public cloud service providers, ensuring frequent deployments without breaking too many things. On top of improving deploy frequency, a DevOps approach can also shorten lead time and provide a faster mean time to recovery. To achieve this, automation tools are essential: Chef and Puppet are great for configuration management, Git is a popular choice for version control, and test systems such as Jenkins, Gradle, and Maven round up the automation of common developer tasks such as creating executables and establishing documentation.
Much like Big Data Engineers, Data Scientists are a new breed of indispensable employees for companies wanting to extract knowledge and insights from data to remain competitive in a number of industries. Labelled as The Sexiest Job of the 21st Century, Data Scientists are constantly up to their ears in data, acting as the world’s technical fortune tellers via analytics, visualisations, pattern recognition, and machine learning, to name just a few of their competencies.
Data Scientists need to know the ropes when it comes to statistical programming languages and are often R or Python fluent. A database querying language like SQL is also part of their arsenal. For Data Scientists working at data-driven companies, machine learning methods such as clustering and decision tree learning will be crucial. Tools such as TensorFlow and scikit-learn contain a variety of machine learning algorithms.
Google has cemented itself as a technological giant in recent years, so it's no wonder their in-house programming language has become a frontrunner for preferred statically typed languages. Go, or Golang as it’s often referred to, is completely open source and was only released in November 2009, after successfully being implemented in some of Google’s production systems.
While Golang Developers are also expected to be proficient in other languages, programming primarily in Go was completely nonexistent five years ago. Considered a new language across the board, it’s basic syntax puts it in the C family, with Pascal and Modula noted as having a significant influence in terms of declarations and packages.