While artificial intelligence continues to dominate the headlines in IT, data management, data science, and analytics continue to be a major force in technology investment and job demand. And while recruitment for data professionals may not be as active as AI at the moment, the need for data professionals remains strong and stable.
“Every time a new AI model emerges, there is a massive data mess to clean up. Data governance and architecture will be the silent protagonists of 2026,” said Matthew Baden, Managing Director of Technology Recruitment at The Search Experience..
Robust job market, but below recent peaks
Hiring for data-related roles remains consistently strong across most industries, although growth has stabilized compared to the explosive demand of the previous year. Why: Many organizations are now close to completing their initial data infrastructure investments. Many of these organizations are now focused on maximizing the ROI on their efforts through better analytics and integration with AI initiatives, explains Kanani Breckenridge, CEO and “Head Huntress” at Kismet Search, a San Diego-based recruiting firm..
Tim Mobley, president of Connext Global, agrees with this assessment.a global staffing company. He says data management and analytics roles will be in highest demand when employers understand the relationship between AI and the data that feeds it.
“Across our workforce, we see us consistently hiring data engineers, governance leaders, and analytics translators who not only build pipelines, but also ensure compliance, security, and usability,” Mobley says.
A changing job market places more emphasis on driving decision-making
The continued demand for skilled data and analytics professionals is confirmed by hiring trends tracked by job search engine company Metaintro.said Lacey Kaelani, CEO of the company.
“The IT market is changing, and we can see that in our internal data,” Kaelani explains. “Companies are cutting back on generalist and entry-level roles and looking for more hands-on roles such as data engineers, cloud data architects, and analytical translators.”
These types of roles marry technical skills with real business needs, and ultimately the entire labor market is moving toward them, Kaelani says. Companies don’t just want administrative support. They want people who can explain what the data means and how it should guide decision-making.
Recruiters also need to understand that data-focused investments and AI technologies are not separate investments, but are strategically linked.
Wanted: Data experts who can connect data to decision-making.
With increased focus on work impact and outcomes, employers are turning to experts who can connect data to decision-making, Mobley said. The most valuable skill set is focused on aligning insights and clarifying key performance indicators, rather than creating dashboards.
“At Connext, we recently conducted a study that found less than one in four employees believe that their KPIs fully capture ‘good work.’ This proves the desire for clearer, real-time, results-based measurement. Analytics professionals and their managers who help close that gap are positioned for long-term growth,” says Mobley.
Some of the most in-demand data-related roles include data governance, architecture, and AI dataset management. Data engineers who can design reliable and scalable pipelines remain in high demand.
Analytical engineers who bridge the gap between traditional data engineering and modern analytical workflows are especially valuable, Breckenridge says. As regulatory requirements continue to expand, data governance specialists are in high demand.
Top rewards go to domain-specific AI data experts
Similar to employment, salaries for data professionals have remained flat since their peak in 2021-2022. “The excitement right now is all about AI,” Baden says. “Candidates with domain-specific AI data experience will be in high demand, and their salaries will increase accordingly.”
Of course, your actual salary rate will depend on location, industry, and years of experience. But data professionals should be able to earn or maintain a very livable wage in most markets, Breckenridge said. In her city of San Diego, senior data engineers typically earn between $150,000 and $230,000, while mid-level professionals earn between $120,000 and $160,000. Nationally, she says, there hasn’t been much change in compensation for data-related roles over the past few years.
“We also see that pure data reporting job opportunities are flattening, impacting pay and benefits, while hybrid roles that combine data with AI and ML skills are earning a 10% to 15% pay premium,” Kaelani says. “Companies are willing to pay more for people like architects who understand when and how to apply AI to internal processes.”
According to professional networking and job site Indeed.Current salaries for top data-related positions at the national level are as follows:
- Senior Data Scientist = $158,536
- Data Engineer = $131,546
- Data Scientist = $129,607
- Senior Data Analyst = $102,988
- Business Intelligence Analyst = $100,256
- Data Analyst = $84,655
Salary premiums are rising for roles that involve regulated data or high-stakes outcomes, particularly in healthcare and finance, Mobley said. Benefits strategies that emphasize inclusion and professional development, including ensuring equal access to learning and assessment for both offshore and US teams, are becoming key to retention.
In terms of benefits, Kaelani says the organization is investing more in office flexibility and learning incentives. This shows that many organizations are realizing how expensive it is to hire talent to get them out of the skills gap crisis. Instead, they are investing more in their current talent.
Breckenridge said professional development benefits such as training budgets and certification support are common, given the rapid evolution of data technology.
The ideal data professional candidate
IT leaders and recruiters are particularly looking for candidates with strong technical training and the ability to work with complex datasets, ideally at an AI-native company for two to three years.
The lines between traditional data analysis and machine learning continue to blur. That means data engineers increasingly need to become familiar with AI and ML concepts and tools to stay competitive, Breckenridge says. Growing regulatory compliance around data privacy and AI governance is creating new, more specialized roles and adding responsibilities to existing data teams.
Breckenridge explains that data-savvy candidates understand both the technical and business aspects of their job. Engineers who can not only build robust data systems, but also translate their analysis into actionable business insights, are most valuable.
Finally, for 2026, Baden says the ideal data candidate will actually be a translator. Someone who is technically proficient and culturally fluent and can help organizations move from a “data everywhere” mindset to “data that matters.”
