Data and analytics hiring in strong demand heading into 2026
Although artificial intelligence continues to grab the lion’s share of headlines in the IT space, data management, data science and analytics continue to be major forces in tech investments and hiring demand. And while hiring for data pros may not be quite as hot as AI at the moment, the need for data professionals remains strong and steady.
“Every new AI model creates a bigger data mess to clean up – data governance and architecture will be the quiet heroes of 2026,” says Matthew Baden, managing director, tech recruitment at The Search Experience.
A strong job market, but below its recent peaks
Hiring for data-related job roles remains consistently strong through most industries, even though growth has stabilized compared to the explosive demand of previous years. The reason: many organizations have largely now completed their initial data infrastructure investments. Many of those organizations are now focused on maximizing ROI for those efforts through better analytics and integration with AI initiatives, explains Kanani Breckenridge, CEO and ‘headhuntress’ at San Diego based recruiting firm Kismet Search.
Agreeing with that assessment is Tim Mobley, president at Connext Global, a global staffing firm. He says the highest demand for data management and analytics roles is when employers understand the relationship of AI with the data feeding it.
“Across the workforce, we can see consistent hiring for data engineers, governance leads, and analytics translators who not only build pipelines but also ensure compliance, security, and usability,” Mobley says.
A shifting job market, with greater emphasis on driving decisions
Continued demand for skilled data and analytics professionals is confirmed by hiring trends tracked at job search engine firm Metaintro, says company CEO Lacey Kaelani.
“The IT market is shifting and we can see it from our internal data,” Kaelani explains. “Companies are cutting down on generalist and entry level roles, and demanding more hands-on roles such as data engineers, cloud data architects, and analytical translators.”
These types of roles connect technical skills with real business needs, which is ultimately what the entire labor market is shifting to, Kaelani says. Companies don’t just want administrative support. They want people who can explain what data means and how it should guide decisions.
Hiring managers should also understand that data-focused investments and AI technologies are not stand-along investments, but strategically related.
Wanted: Data pros that can connect data to decisions
With this growing focus on work impacts and results, employers are leaning on professionals who can connect data to decisions, Mobley says. The most valuable skillsets are less about producing dashboards, and more about aligning insights to clear key performance indicators.
“At Connext, we recently conducted research that showed that less than one in four employees believe their KPIs fully capture ‘good work,’ proving an appetite for clearer, real-time, outcome-based measurements. Analytics pros and their managers who help bridge that gap are positioned for long-term growth,” Mobley says.
Data-related roles that are most in demand include those in data governance, architecture, and dataset management for AI. Data engineers who can design reliable, scalable pipelines remain in high demand.
Analytics engineers who bridge traditional data engineering with modern analytics workflows are particularly valuable, Breckenridge says. Data governance specialists are seeing increased demand as regulatory requirements continue to expand.
Top pay goes to domain-specific AI data pros
Just as with hiring, salaries for data professionals have flatlined since the 2021–2022 peak. “The heat is all in AI right now,” Baden says. “Candidates with domain-specific AI data experience will see rising demand and corresponding salary growth.”
Actual pay rates are determined by location, industry and years of experience, of course. But data pros should be able to acquire or maintain a very liveable wage in most markets, Breckenridge says. In her city of San Diego, senior data engineers typically earn between $150,000 and $230,000, with mid-level professionals ranging from $120,000 to $160,000. Nationally, there hasn’t been much change in compensation for data roles over the past several years, she says.
“We also are seeing that opportunities for pure data reporting jobs are flattening, impacting pay and benefits, while hybrid roles – data plus AI and ML skills – are earning a 10% to 15% pay premium,” Kaelani says. “Companies are willing to increase salaries for people who understand when and how to apply AI into their internal processes, such as architects.”
According to professional networking and job site Indeed, current national level salaries for top data related jobs 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
Pay premiums are rising for roles close to regulated data and high-stakes outcomes, especially when you look at healthcare and finance, Mobley explains. Benefits strategies that emphasize inclusion and professional development, such as ensuring both offshore and U.S. teams get equal access to learning and recognition, are becoming key to retention.
Regarding benefits, Kaelani says organizations are investing more in office-flexibility and learning stipends. This illustrates that many organizations recognize how expensive it can be to hire their way out of the skills gap crisis. Instead, they’re invest more in their current talent.
Professional development benefits, including training budgets and certification support, are common given the rapid evolution of data technologies, Breckenridge says.
The ideal data pro candidate
IT leaders and hiring managers are especially looking for job candidates who can show strong technical training, plus two to three years working with complex data sets, ideally in an AI-native company.
The boundary between traditional data analytics and machine learning continues to blur. Meaning that data engineers increasingly need familiarity with AI and ML concepts and tools to remain competitive, Breckenridge says. Expansion of regulatory compliance around data privacy and AI governance is also creating newer, specialized roles and adding responsibilities for existing data teams.
Strong data candidates understand both the technical and business dimensions of their work efforts, Breckenridge explains. Engineers who can build robust data systems but also translate analytical findings into actionable business insights are most valuable.
Finally, heading into 2026, Baden says the ideal data candidates will really be translators: technically skilled, culturally fluent, and able to help organizations shift from the “data everywhere” mindset to “data that matters.”
