The 17 fastest-growing, highest-paying tech skills (no certification required)

Employers are more apt to pay extra cash for noncertified tech skills compared to certifications. Here are the ones they covet the most right now, and why.

top it skills primary

My last column reviewed cash pay premiums for tech certifications in the second half of 2019, specifically which ones pay exceptionally well and are still growing in market value. But did you know that employers are more willing to pay cash premiums for as many as 578 tech skills that are not certified? Either there are no certifications available for these skills or it just doesn’t matter to the employers that are investing in them.

That’s a lot of skills. And the survey demographics behind them are impressive, there are 78,138 U.S. and Canadian tech professionals in as many as 3,510 private and public sectors earning dollars for their certified and noncertified skills. We’ve tracked and reported them since 2000 in our quarterly-updated IT Skills and Certifications Pay IndexTM (ITSCPI).

Beware the ranking of hot skills

You may have noticed there is no shortage of tech-related top 10 lists of hot jobs, hot skills, hot cities, and best employers for tech professionals. You have to wonder though how these lists were created.

Hot skills and certifications articles featuring top 10 lists typically . The problem is salaries are paid to workers for entire jobs, not just specific in-demand skills or certifications. Using this methodology, is it even possible to separate how much of a salary is earmarked for a specific certified or noncertified skill and how much is paid for the various duties and responsibilities, knowledge and competencies, or necessary soft skills? No attempt is ever made to ferret this out.  

Another popular source of ranked hot skills lists are jobs boards (e.g., Dice, Hired, Monster, Indeed) that use keyword searches to tally the number of mentions in job postings and then rank them by volume. Still another methodology is employed by recruiting firms that survey executives or track compensation data from their job placements. As long as you’re savvy enough to understand how the results may be skewed based on factors such as data sample demographics, survey methodologies, timing and bias these approaches have their strengths and weaknesses.

 Foote Partners takes a traditional  research approach, independently tracking and reporting individual jobs, skills and certifications performance for more than two decades with the simple goal of vendor independence, higher than average reliability data validation and, above all, the most accuracy attainable.

And the winners are…

The following noncertified tech skills meet two prerequisites: They are earned workers cash pay premiums well above the average of all 578 skills reported in our firm’s ITSCPI, plus they recorded gains in cash market value in the six months ending January 1, 2020. No skill below is earning less than the equivalent of 15 percent of base salary — significant considering the average for all skills reported is 9.5 percent of base — and are listed in descending ranked order of cash premium earned and market value increases (including ties).

Big data analytics

Market Value Increase: 11.8 percent  (in the six months through January 1, 2020)       

Big data analytics related skills and certifications have grown in market value every quarter in the past two years. Cash premiums for 106 big data related noncertified skills have, as a group, increased 4.6 percent in market value in the past 12 months, averaging the equivalent of 12.3 percent of base salary.

For all the interest in the use of advanced data analytics to enable companies to understand, package and visualize data for enhanced decision-making, the truth is that the marketplace for so-called big data skills has been surprisingly volatile: 37 (or 35 percent ) of big data skills tracked in our benchmark research changed market value in the third quarter of 2019.

Prescriptive analytics

Market Value Increase: 35 percent (in the six months through January 1, 2020)           

Prescriptive analytics, an area of business analytics dedicated to finding the best course of action for a given situation, is related to both descriptive and predictive analytics. While descriptive analytics aims to provide insight into what has happened and predictive analytics helps model and forecast what might happen, prescriptive analytics seeks to determine the best solution or outcome among various choices given the known parameters. It can also suggest decision options for how to take advantage of a future opportunity or mitigate a future risk, and illustrate the implications of each decision option. In practice, prescriptive analytics can continually and automatically process new data to improve the accuracy of predictions and provide better decision options.

Specific techniques used in prescriptive analytics include optimization, simulation, game theory and decision-analysis methods. Advancements in the speed of computing and the development of complex mathematical algorithms applied to the data sets have boosted demand for prescriptive analysis skills. 

Prescriptive analytics can be used in two ways:

  • Inform decision logic with analytics. Decision logic needs data as an input to make the decision. The veracity and timeliness of data will ensure that the decision logic will operate as expected. It doesn’t matter if the decision logic is that of a person or embedded in an application — in both cases, prescriptive analytics provides the input to the process. Prescriptive analytics can be as simple as aggregate analytics about how much a customer spent on products last month or as sophisticated as a predictive model that predicts the next best offer to a customer. The decision logic may even include an optimization model to determine how much, if any, discount to offer to the customer.
  • Evolve decision logic. Decision logic must evolve to improve or maintain its effectiveness. In some cases, decision logic itself may be flawed or degrade over time. Measuring and analyzing the effectiveness or ineffectiveness of enterprises decisions allows developers to refine or redo decision logic to make it even better. It can be as simple as marketing managers reviewing email conversion rates and adjusting the decision logic to target an additional audience. Alternatively, it can be as sophisticated as embedding a machine learning model in the decision logic for an email marketing campaign to automatically adjust what content is sent to target audiences.


Market Value Increase: 12.5 percent (in the six months through January 1, 2020)   

DevSecOps is the philosophy of integrating security practices within the DevOps process and involves creating a ‘Security as Code’ culture with ongoing, flexible collaboration between release engineers and security teams. It’s a natural and necessary response to the bottleneck effect of older security models on the modern continuous delivery pipeline. The goal is to bridge traditional gaps between IT and security while ensuring fast, safe delivery of code. Silo thinking is replaced by increased communication and shared responsibility of security tasks during all phases of the delivery process.

In DevSecOps, two seemingly opposing goals – “speed of delivery” and “secure code” – are merged into one streamlined process. In alignment with lean practices in agile, security testing is done in iterations without slowing down delivery cycles. Critical security issues are dealt with as they become apparent, not after a threat or compromise has occurred.

Here are six important components of a DevSecOps approach:

  • Code analysis – deliver code in small chunks so vulnerabilities can be identified quickly.
  • Change management – increase speed and efficiency by allowing anyone to submit changes, then determine whether the change is good or bad.
  • Compliance monitoring – be ready for an audit at any time (which means being in a constant state of compliance, including gathering evidence of GDPR compliance, PCI compliance, etc.).
  • Threat investigation – identify potential emerging threats with each code update and be able to respond quickly.
  • Vulnerability assessment – identify new vulnerabilities with code analysis, then analyze how quickly they are being responded to and patched.
  • Security training – train software and IT engineers with guidelines for set routines.

Security architecture & models

Metadata design and development

Market Value Increase: 5.9 percent (in the six months through January 1, 2020)           

Two fundamental concepts in computer and information security are the security model, which outlines how security is to be implemented – in other words, providing a “blueprint” – and the security architecture of a computer system, which fulfills this blueprint. Security architecture is a view of the overall system architecture from a security point and how the

system is put together to satisfy the security requirements. It describes the components of the logical hardware, operating system, and software security components, and how to implement those components to architect, build and evaluate the security of computer systems. With cybersecurity related skills gaining nearly 3 percent in cash market value in the past year and the threat landscape continuing to be a core business issue, we expect security models and architecting skills to continue to be strong going forward.

Data can be replicated and delivered anywhere in the world instantaneously – it is the fundamental resource in the new economy. The business rocket ship known as digital innovation depends on data, metadata, and artificial intelligence (AI) working in concert to create systems that gets smarter over time. But while data is used to drive decision-making and insights, it’s the metadata that stores what is learned – what works, when to use it, what is still uncertain – and this the key to “smarter.” Digital transformation is driving a new wave of interest in metadata design and development skills in the last year.

Smart contracts

Market Value Increase: 13.3 percent (in the six months through January 1, 2020)           

Smart contracts help you exchange money, property, shares, or anything of value in a transparent, conflict-free way while avoiding the services of a middleman. They’re the product of the decentralized ledger systems that run the blockchain and so skills in smart contracts are be catapulted along with Ethereum and others for an almost unlimited number of uses ranging from financial derivatives to insurance premiums, breach contracts, property law, credit enforcement, financial services, legal processes and crowdfunding agreements.

Data Engineering

Market Value Increase: 6.3 percent (in the six months through January 1, 2020)           

Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In order for that work to ultimately have any value, there also have to be mechanisms for applying it to real-world operations in some way. Those are both engineering tasks: the application of science to practical, functioning systems.

Neural networks

Market Value Increase: 14.3 percent (in the six months through January 1, 2020)           

Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated and they help cluster and classify.

You can think of them as a clustering and classification layer on top of the data you store and manage. They help to group unlabeled data according to similarities among the example inputs, and they classify data when they have a labeled dataset to train on. Neural networks can also extract features that are fed to other algorithms for clustering and classification, so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning, classification and regression.

Apache Zookeeper

Amazon DynamoDB

Master data management

Data science



Market Value Increase: 6.7 percent (in the six months through January 1, 2020)           

Apache ZooKeeper is essentially a service for distributed systems. It offers a hierarchical key-value store used to provide a distributed configuration service; a synchronization service; and a naming registry for large distributed systems. Introduced as a sub-project of Hadoop, it is now a top-level Apache project in its own right. ZooKeeper's architecture supports high availability through redundant services. The clients can thus ask another ZooKeeper leader if the first fails to answer. ZooKeeper nodes store their data in a hierarchical name space, much like a file system or a tree data structure. Clients can read from and write to the nodes and in this way have a shared configuration service.

Some of the prime features of Apache ZooKeeper are the following:

  • Reliable System: This system is very reliable as it keeps working even if a node fails.
  • Simple Architecture: The architecture of ZooKeeper is quite simple as there is a shared hierarchical namespace which helps coordinating the processes.
  • Fast Processing: ZooKeeper is especially fast in "read-dominant" workloads (i.e. workloads in which reads are much more common than writes).
  • Scalable: The performance of ZooKeeper can be improved by adding nodes.

Amazon DynamoDB, part of the Amazon Web Services portfolio, is a fully managed proprietary NoSQL database service supporting key-value and document data structures. DynamoDB exposes a similar data model to (and derives its name from) Dynamo, but has a different underlying implementation. It uses synchronous replication across multiple data centers for high durability and availability and differs from other Amazon services by allowing developers to purchase a service based on throughput rather than storage. Administrators can request throughput changes and DynamoDB will spread the data and traffic over a number of servers using solid-state drives, allowing predictable performance. It offers integration with Hadoop via Elastic MapReduce.

Master data management (MDM) arose out of the necessity for businesses to improve the consistency and quality of their key data assets, such as product data, asset data, customer data, location data, etc. Many businesses today, especially global enterprises, have hundreds of separate applications and systems where data that crosses organizational departments or divisions can easily become fragmented, duplicated and most commonly out of date. When this occurs, accurately answering even the most basic but critical questions about any type of performance metric or KPI for a business becomes hard. The basic need for accurate, timely information is acute and as sources of data increase, managing it consistently and keeping data definitions up to date so all parts of a business use the same information is a never-ending challenge. That’s what has and will continue to drive a premium on MDM skills.

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.  Data science is the same concept as data mining and big data: using the most powerful hardware, the most powerful programming systems, and the most efficient algorithms to solve problems. Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.

The Scala programming language – short for ‘scalable’ – makes up for a lot of deficiencies in Java, integrating with Java while optimizing code to work with concurrency. It appeals most to enterprises that have already invested in Java and don't want to have to support anything new in their production environments.

Cryptography (or cryptology) is the practice and study of techniques for secure communication in the presence of third parties called adversaries. More generally, cryptography is about constructing and analyzing protocols that prevent third parties or the public from reading private messages. Various aspects in information security such as data confidentiality, data integrity, authentication, and non-repudiation are central to modern cryptography. Modern cryptography exists at the intersection of the disciplines of mathematics, computer science, electrical engineering, communication science, and physics. Applications of cryptography include electronic commerce, chip-based payment cards, digital currencies, computer passwords and military communications.

Today cryptography is heavily based on mathematical theory and computer science practice. Cryptographic algorithms are designed around computational hardness assumptions, making such algorithms hard to break in practice by any adversary. It is theoretically possible to break such a system, but it is infeasible to do so by any known practical means. These schemes are therefore termed ‘computationally secure’; theoretical advances, e.g., improvements in integer factorization algorithms, and faster computing technology require these solutions to be continually adapted. There exist information-theoretically secure schemes that provably cannot be broken even with unlimited computing power – an example is the one-time pad – but these schemes are more difficult to use in practice than the best theoretically breakable but computationally secure mechanisms.


National Institute of Standards and Technology (NIST)      

Amazon Kinesis

Market Value Increase: 7.1 percent (in the six months through January 1, 2020)           

TensorFlow is a popular open-source deep learning library developed at Google, which uses machine learning in all of its products to take advantage of their massive datasets and improving the search engine, translation, image captioning and recommendations. TensorFlow is also used for machine learning applications such as neural networks. Its flexible architecture allows for the easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. TensorFlow provides stable Python and C APIs without API backwards compatibility guarantees for C++, Go, Java, JavaScript and Swift. Third-party packages are available for C#, Haskell, Julia, R, Scala, Rust, OCaml and Crystal.

Python has always been the choice for TensorFlow due to the language being extremely easy to use and having a rich ecosystem for data science including tools such as Numpy, Scikit-learn, and Pandas.

The National Institute of Standards and Technology (NIST) is a non-regulatory agency of the United States Department of Commerce whose mission is to promote innovation and industrial competitiveness. NIST's activities are organized into laboratory programs that include nanoscale science and technology, engineering, information technology, neutron research, material measurement, and physical measurement.

Driving demand most for NIST expertise in the IT marketplace right now is arguably its Cybersecurity Framework which provides a policy framework of computer security guidance for how private sector organizations can assess and improve their ability to prevent, detect, and respond to cyberattacks. It provides a high-level taxonomy of cybersecurity outcomes and a methodology to assess and manage those outcomes and is being used by a wide range of businesses and organizations to help shift organizations to be proactive about risk management.

Amazon Kinesis is an Amazon Web Service (AWS) for processing big data in real time. Kinesis is capable of processing hundreds of terabytes per hour from high volumes of streaming data from sources such as operating logs, financial transactions and social media feeds. Kinesis fills a gap left by Hadoop and other technologies that process data in batches, but that don't enable real-time operational decisions about constantly streaming data. That capability, in turn, simplifies the process of writing apps that rely on data that must be processed in real time.