ADJACENT ROLES
There is increased immersion of renaissance data managers with adjacent roles in their enterprises—either through assuming new skills or collaborative approaches to data challenges. There are roles such as software developers, data architects, database reliability engineers, cloud architects, data scientists, platform engineers, and DevOps engineers and software development teams, said Atwell.
These adjacent professionals “are starting to handle a lot of the infrastructure and configuration components of databases,” McElroy said. “The vast improvements in database source control have given development teams better control over managing database change without keeping a DBA up all night for small-to-medium applications.”
Such converged roles “reflect how databases are now part of automated, cloud-native, and AI-assisted systems rather than standalone infrastructure,” said Chin.
For example, developers are finding it easier to incorporate databases into their projects. Areas such as “infrastructure, configuration, and even things like developers more than ever,” McElroy said.
Add to that the ongoing “democratization of deeper knowledge about database engines and their inner workings. Some of the sacred knowledge that once resided only in a DBA’s head is now available to everyone.”
DevOps—the merging of development and operations roles—has served as a catalyst for this convergence between roles.
“DBAs are working with platform teams to empower developers to safely self-service database changes while ensuring minimal downtime, zero data loss, and data consistency,” said Atwell.
ADAPTING AND LEARNING
Those aspiring or seeking to expand their careers as renaissance data managers need to think of their profession in terms of continuous learning. Think in terms of becoming an “AI wrangler,” Steve Zisk, principal data strategist at Redpoint Global, suggested. “AI can easily go off the rails if it doesn’t understand its limitations or even understand query optimization. AI may build a query that’s accurate, for example, but may cost 10 times the data and compute an individual would build.”
DBAs need to “think of themselves as supervisors of ‘dumb interns,’ with AI being the intern in this analogy,” Zisk said. “The AI wrangler of tomorrow needs to start thinking in terms of how to seamlessly bring various components together to work together, to include AI, the customer’s AI, the end customer, and even the DBA and any other internal data stakeholders.”
Expand beyond a single database technology, Joshi urged. “Key areas include cloud platforms, automation, security- by-design, and performance engineering, plus a working understanding of AI and data pipelines. Familiarity with specialized databases such as time series for IoT [Internet of Things] or vector databases for generative AI is increasingly important. Those who evolve toward data architecture, platform leadership, or AI operations will have strong long-term career prospects.”
For data managers, “Adaptability has less to do with giving up their skill sets altogether and more with building on them,” said Riken Shah, founder and CEO at OSP. “Cloud savvy, infrastructure knowledge and security awareness are now table stakes. Understanding how data supports analytics and AI workloads is increasingly important. Skills in scripting, automation, and data governance create durable career paths.”
The database is still at the center, Shah continued, “but responsibility for it has expanded now. Those who evolve with that scope will continue to play a critical role in how organizations operate and scale.”
With the rise of AI, it’s important for data managers to “learn how to deploy and manage databases using AI-driven interfaces, such as MCP [Model Context Protocol] servers that allow AI systems to interact directly with infrastructure and data platforms,” Chin advised. “This means understanding how databases can be provisioned, scaled, and queried through AI agents instead of traditional dashboards or scripts. Practitioners can start by experimenting with MCP-based tools in test environments to see how AI-driven orchestration changes day-today operations.”
AI speeds up delivery, “but it also speeds up mistakes,” cautioned Ryan McCurdy, VP of marketing at Liquibase. “The best career move is to become the person who makes change safe. That means CI/CD fluency, policy enforcement early in the pipeline, automated evidence, drift detection, and fast, controlled recovery. In an AI-driven environment, the schema becomes a reliability boundary. DBAs who can govern change at that boundary will be in higher demand, not lower.”
In today’s AI environments, data managers must evolve from “basic database administration to end-to-end ownership and focus on items that are ever increasingly of strategic importance,” said Grant. “Instead of looking at databases in isolation, DBAs need to master the SRE model by replacing manual, repetitive tasks with engineering-driven automation and infrastructure as code.”
Simply understanding cloud and database infrastructure is now “table stakes,” Grant added. “With the proliferation of AI, DBAs should be able to complete routine, tedious work faster than ever, freeing them up to focus on high-level strategic items. To adapt, DBAs have to get out of their silos, collaborate directly with engineering teams, and innovate to see the full scope of what’s possible.”
Far from being dated, data managers are more critical than ever, Banthia agreed. “The surface area of risk has expanded dramatically: Availability expectations are higher, costs are more visible, security is nonnegotiable, and infrastructure decisions directly impact business velocity. The modern DBA is no longer measured by how well they maintain a database, but by how effectively they enable scale, resilience, and control across hundreds of databases with minimal human intervention.”
Welcome to the age of the renaissance data manager.