10 Highest-Paying IT Job Skills
Adding one of these capabilities to your resume might lead to a salary increase.
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IT publications often publish reports about the highest-paying job titles, but that information can be of limited use if you are already working in IT and not interested in switching to a new specialty. For example, going from being a database administrator to a cybersecurity specialist, is a pretty major shift. It probably will require a whole lot of training, and you might even need to take a temporary pay cut before you earn the higher salary that prompted the change.
But sometimes you don't need to switch jobs to boost your income. Learning just one or two new skills might boost your earning potential without requiring you to move to an entirely new area of expertise.
Foote Partners' IT Skills and Certification Pay Index tracks the pay premiums for certified and non-certified skills. In the report from the first quarter of 2018, the 522 non-certified tech skills Foote tracks were particularly valuable. Having just one of these skills increased an IT worker's pay by an average of 9.3%. By comparison, the 446 IT certifications Foote tracks boosted salaries by an average of 7.6%.
Why are non-certified skills so important?
As companies embrace digital transformation, they are finding that they need employees who understand emerging technologies like blockchain, the Internet of Things (IoT), artificial intelligence, machine learning and advanced data analytics. These technologies are so new that few, if any, certifications are available.
In addition, digital transformation is driving employers to seek out staff members who can see the big picture and recommend large-scale changes to the way IT operates that could make the company more profitable. They are looking for IT professionals with architecture and business skills who can help guide the company through these difficult transitions.
The following slides detail the top 10 highest-paying noncertified IT skills from the Foote Partners report. Based on the data, adding one of these skills to your resume could boost your take-home pay. The skills were ranked by Foote Partners.
Companies in every industry are endeavoring to become more data-driven. To that end, they need people with data architecture skills. These are people who can understand the complex relationships among databases, applications and systems, and design new ways to store and manage data. People with data architecture skills may actually have the title "data architect" or they may work as database administrators, database managers, application developers, data scientists, project managers or even business analysts.
According to the Robert Half Technology 2019 Salary Guide for Technology Professionals, salaries for data architects in the United States range from $111,500 in the 25th percentile to $187,750 in the 95th percentile. Those in the 50th percentile earn $133,500.
Cybersecurity continues to be a highly in-demand field as enterprises struggle to stay ahead of cybercriminals' ever-changing strategies and tactics. Today's organizations often have deployed a complex array of defensive measures, and as in the data management field, they need professionals who can understand the bigger picture and the way these disparate tools work together. According to the SANS Institute, "The objective of enterprise security architecture is to provide the conceptual design of the network security infrastructure, related security mechanisms, and related security policies and procedures. The enterprise security architecture links the components of the security infrastructure as one cohesive unit."
People with these security architecture and modelling skills may be called security architects, cybersecurity managers, security administrators, security analysts or similar titles. These cybersecurity professionals tend to be very well compensated. For example, Robert Half data places the median salary for U.S. data security analysts at $125,250.
Complex event processing (CEP) is a particular type of data processing that seeks to find correlations among data from multiple systems. It's a concept that has been around in technology circles since the 1990s, but it is finding particular relevance today in relation to big data analytics, IoT, cybersecurity and related areas. For example, in an industrial IoT deployment, a system may receive alerts that multiple pieces of equipment went offline near the same time. A CEP system would analyze the streaming data from multiple sensors and attempt to find the root cause of the problem. Similarly, CEP systems used for cybersecurity would be able to correlate multiple alerts received by different security tools so that analysts could identify the nature of an ongoing cyberattack. As enterprise IT environments become increasingly complex, the demand for CEP engines and systems is increasing.
Machine learning is the branch of artificial intelligence that focuses on getting systems to improve their performance on a task without being explicitly programmed. It has applications in several of the hottest areas of technology today. For example, many big data analytics systems use machine learning to spot patterns in data and improve their predictive capabilities. Cybersecurity tools use machine learning to boost their ability to detect, prevent and mitigate attacks. IoT management tools use machine learning to detect and respond appropriately to anomalies. And ecommerce sites use recommendation engines based on machine learning to make suggestions regarding future purchases. With so many different applications for this technology, it's no surprise that both the Robert Half report and the Foote Partners report call out machine learning as one of the most valuable skills that IT professionals can add to their resumes.
Closely related to machine learning is the realm of predictive analytics and prescriptive analytics. This type of software analyzes historical data, finds trends and patterns, and forecasts future events based on the results. This type of software is becoming increasingly common among enterprise users. Often viewed as an extension of predictive analytics, prescriptive analytics actually generate suggested actions based on predictive analytics results.
In IDC's June 2018 Worldwide Advanced and Predictive Analytics Software Forecast, Chandana Gopal, research manager, Analytics and Information Management, wrote "The APA software market, which in 2017 reached $3.1 billion worldwide, is expected to grow at a five-year CAGR of 9.4%. Sophisticated analytical techniques are being embedded into more and more applications. Forward-looking analytics is going to become much more mainstream, as enterprises are able to harness more and more data from a variety of sources." Knowledge of predictive analytics can be helpful for IT professionals involved in data management, cybersecurity, application development, IT operations and many other areas.
Businesses have always faced risk — that isn't anything new. However, today's organizations are under increasing pressure to quantity and manage that risk, and that's where risk analytics and assessment comes in.
Risk analytics tools use technology like predictive analytics and machine learning to better understand the various types of risk an organization might face and provide direction to business leaders on steps to take to minimize that risk.
Like several of the skills on this best-paying list, risk analytics requires both technical and business knowledge. This capability can be helpful for analysts, data managers, IT managers, application developers and other IT professionals.
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