Main image of article 5 Non-Certified Tech Skills Enjoying a Boost in Market Value

What skills can translate into higher pay? That’s a key question for many technologists, especially as we head into 2022. Fortunately, a new analysis by research group Foote Partners provides some much-needed guidance on which skills can translate into a significant pay premium.

Foote Partners generates a quarterly breakdown of pay trends for IT skills and certifications. Their Q4 2021 research update offers what some might view as unsettling news: the average market value of some 630 non-certified tech skills decreased slightly in the third quarter of 2021. But as the report points out, not all pay decreases are due to slackening demand for a particular skill: “A quarterly decline in pay for a skill may signal that the market supply of talent for that skill is catching up to demand.” 

It’s a similar issue with tech certifications; for years, Foote Partners has tracked how the pay premiums associated with certain certifications have trended down as those certifications have become more popular, fulfilling demand. Between July and October of this year, the report also noted, the average market value increased for 555 tech certifications.

In a similar vein, some non-certified tech skills also enjoyed a jump in pay premiums and market value increases. But which ones? Here’s a breakdown of a few of the top ones; for the rest, check out the Foote Partners report.

Risk analytics/assessment

Average pay premium: 20 percent of base salary equivalent
Market value increase: 5.3 percent (in the six months through Oct. 1, 2021)

Risk analytics is a key element of business intelligence. Those skilled in it can analyze data to model various types of risk (such as fraud risk, credit risk, market risk, and so on). While this isn’t a “tech skill” in the same way as, say, AWS or Azure, it leverages a variety of tech skills and tools, including machine learning.


Average pay premium: 19 percent of base salary equivalent
Market value increase: 11.8 percent (in the six months through Oct. 1, 2021)

While many folks are familiar with Ethereum as a cryptocurrency, this open-source, public blockchain could also play a major role in the mainstreaming of “smart contracts,” NFTs, and other, cutting-edge technologies.  


Average pay premium: 18 percent of base salary equivalent
Market value increase: 25 percent (in the six months through Oct. 1, 2021)

An open source NoSQL database, HBase can scale to accommodate massive amounts of data, and it can incorporate vastly different data structures. Foote Partners cites HBase’s usefulness in applications where “strong consistency is important.”

Amazon Dynamo DB
Oracle Exadata

Average pay premium: 18 percent of base salary equivalent
Market value increase: 12.5 percent (in the six months through Oct. 1, 2021)

These database technologies play a vital role in many organizations’ data buildouts. DynamoDB “uses synchronous replication across multiple data centers for high durability and availability,” according to Foote Partners, while Oracle Exadata (which is optimized for running Oracle database workloads) “allows mixed workloads to share system resources fairly with resource management features allowing prioritized allocation.”


Average pay premium: 18 percent of base salary equivalent
Market value increase: 5.9 percent (in the six months through Oct. 1, 2021)

MLOps is the practice of deploying and maintaining machine learning models. “When an algorithm is ready to be launched, MLOps is practiced between Data Scientists, DevOps, and Machine Learning engineers to transition the algorithm to production systems,” Foote Partners states. Given the growing importance of machine learning to all kinds of organizations, it’s perhaps no surprise that MLOps is attracting a significant pay premium. 

Many of the other skills cited in the Foote Partners’ report also deal with databases, data analytics, and associated disciplines (such as cybersecurity). When figuring out what to do next in your career, just remember: You can’t go wrong by focusing on storing, wrangling, and analyzing data for key insights.