Beyond the Job Title: Research Data Librarian

A library tech job interview with Kaylee Alexander-Leunissen

Posted on July 8, 2026 in Job Profiles

Core to LibTech Insights’s mission is demystifying the broad and dynamic field of tech librarianship in higher ed. In this series, we interview a librarian every month to learn a little more about their position. As library tech jobs proliferate, they sometimes come with unfamiliar, jargony, or intimidating titles. We want to go beyond the title and look at the responsibilities, skills, and joys that make up the job. We hope this series will increase your knowledge of library tech jobs and skills and offer you greater insight into the working lives of your colleagues.

For this installment, we spoke to Kaylee Alexander-Leunissen to learn more about her job as a Research Data Librarian. Check out our archive of job profiles. 💫

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What is your job title and responsibilities?

I am a Research Data Librarian at the University of Utah’s J. Willard Marriott Library, where my main responsibilities include providing campus-wide instruction related to best practices for research data management, ethical and secure data use, and data visualization. I also provide 1:1 and small-group consultations related to these topics and support for students and faculty engaged in digital humanities research. Another aspect of my job is managing our institutional data repository, which includes not only helping researchers share their data openly to comply with federal grant agency and publisher requirements but also strategizing about data-sharing infrastructure and data curation support for researchers.

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Can you describe your career trajectory leading up to this position?

Arriving at this position was indirect, to say the least. I did my undergraduate and graduate studies in Art History and Visual Culture. While I started with very traditional training in the field at NYU and the Institute of Fine Arts, I ultimately turned toward a “more radical” art history at Duke University. There, I completed my Ph.D. with a dissertation on French cemeteries and the market for vernacular tomb markers in 19th-century Paris.

Early in my Ph.D. program, I was assigned to TA for a class on the history of art markets. I became fascinated by the professor and his team’s approach to using aggregates of data and methods drawn from the social sciences to approach lesser-known histories of art—particularly for studying works for which no material traces remained. At the time, I was starting to develop a project around funerary monuments and exploring the burial regulations instituted by Napoléon in the early 19th century (I know, thrilling stuff). I quickly realized there was a lot to explore regarding survival bias in French cemeteries, especially in places like Père-Lachaise, which became major tourist attractions because of the monumental tombs of notable individuals.

Very long story short, I ended up compiling several large (at least by art historical standards) datasets based on archival documents, which drew me deeper into digital and data-driven humanities methods (and, in retrospect, libraries). While still a Ph.D. candidate, I also worked part-time with Duke University Libraries’ Center for Data and Visualization Sciences as a Digital Humanities Graduate Assistant. In that role, I helped develop and lead workshops using R for text analysis, and assisted other researchers in organizing, cleaning, and wrangling humanistic data for visualization and analysis.

After finishing my Ph.D., I applied for (and received very few responses to) countless postdocs and assistant professorships in art history departments. Just as I was about to give up on academia entirely, I received an invitation to interview for an Emerging Voices Postdoctoral Fellowship from the American Council of Learned Societies (ACLS). They paired me with two universities to interview with—and let’s just say I was way more excited about the school that wasn’t the University of Utah.

Now, nearly five years later, I am so happy that other school (who shall remain nameless) didn’t make an offer and the University of Utah did. I spent the 2022–23 academic year as the ACLS postdoc for Digital Matters, the digital humanities program housed in the Marriott Library. Under the mentorship of the program’s director, Rebekah Cummings, I began realizing that it was libraries—not an academic department—where I really saw myself building a career and being able to make a real impact. So, when the library opened a call for two new Research Data Librarians at the end of my first year, I took the risk and applied. That meant ending my postdoc a year early, but it allowed me to slip into a really dynamic role where I am not confined to just one field of research. I can interact with orthopedists one day, city planners the next, and—since being in this position means I am also tenure-track faculty—still play with death and data during my own research time.

Despite what seems to be a bit of a disciplinary mismatch, my training in art history and digital humanities has been irreplaceable in my role as Research Data Librarian. It allows me to approach data and information science through a humanistic lens. This gives me a unique perspective, whether I’m teaching humanists how to work with data or guiding scientists how to display data in effective visual formats.

It sounds like you’re juggling a lot of responsibilities, from instruction to consultations to much-coveted research time and all your data repository work. What does a typical week look like for you?

There are a lot of different tasks that I manage in a week (and things vary a lot from week to week), but it’s all organized chaos.

The Hive (our data repository) is my team’s bread-and-butter work. Sometimes, we’ll go weeks without a deposit; other times, we find ourselves juggling several in a day. Deposits vary greatly in complexity. Some are very straightforward and can be finished in about an hour—README reviewed, metadata checked, DOI minted, data published. Others will take a few days of going back and forth with the patron to understand their data and make sure we have the best documentation possible. Others still can take weeks.

Beyond active repository reviews, I do a lot of consulting on different projects. Sometimes, it’s an ongoing relationship where I might even end up as an author on the paper; other times, it’s a one-off where someone just needs help getting started with a Data Management and Sharing Plan, figuring out data visualization software, or coming up with a data collection strategy. I probably average about two consults a week. I don’t teach every week, but between single sessions tailored to specific groups and the regular teaching I do for our Research Data Management Certificate program, it works out to about two workshops per month. I try to leave some time each week for teaching prep. Even if it’s a program I’ve done several times, I like to review and update my slides to keep things current. Things can change really fast in data management, especially when working with federal requirements.

When I’m not dealing with immediate patron requests, I focus on long-term infrastructure projects and initiatives. This past year, for example, I’ve been working with our developers to migrate our data repository to a new system that not only improves our UI, but makes our metadata significantly more robust, allows for integrations with ORCID, and improves data harvesting. We’re nerds, so we’ve been stoked about it. In a typical week, if we’re not meeting to discuss strategy, I’m reviewing the staging site, thinking about new workflows, and making a wish list of features to talk about with our developers.

Other tasks that come up less frequently, but are still fun, involve testing out new products or databases the library is considering acquiring. This means sitting in on a demo or sales call every so often and then discussing with our collections development team whether there is a real need. If we acquire something data-specific, I often contribute to internal workflows and public-facing documentation, like LibGuides. A colleague recently referred to me as a “Documentation Deity,” and I’m here for it.

The rhythm of all this work shifts heavily based on the academic calendar. We often get a wave of deposits at the end of the fall and spring semesters—likely due to folks wrapping up teaching commitments and having a bit of extra time to take care of research-related tasks. Summers, on the other hand, tend to be much slower in terms of instruction and research consultations, so that’s when I like to catch up on research and writing and start prepping for any new workshops I might be taking on in the coming academic year.

As a librarian whose career bypassed the MLIS, what advice do you have for people with an academic background interested in working in a library job but without library-school training or credentials?

The best piece of advice I have is to be open about your training and where you have gaps. Libraries are welcoming spaces, and librarians are eager to share their expertise—it’s literally our job. It is always better to be upfront about what you don’t know and actively invite others’ input. On the other hand, it’s so easy to fall into the imposter syndrome trap when you don’t have an MLIS. To counter that, you need to focus on the training you do have. Figure out which of your skills map onto librarianship in meaningful ways and trust that your unique academic experiences give you valuable insights into what your patrons need.

How we can apply a “humanistic lens” to technology has been on many people’s minds lately. How has this position shaped your understanding of this issue?

Being in a role where I deal primarily with those who are not directly engaged in humanities research has made it even more clear how valuable a humanities background can be in any area of research—especially now as we reckon with increasing public accessibility of and reliance on generative AI.

Coming into this role with both visual and data literacy skills was a unique advantage. Prior to becoming a data librarian, I taught and mentored students on digital humanities methods. It was rewarding because humanities students gained hands-on experience with industry-standard data software, adding real-world skills to their résumés in a job market that heavily favors STEM. When I started in my current role, I found the tables turned: I was teaching visual literacy to researchers in the sciences and health sciences.

I developed a short course for our Research Education program on ethical data visualization and graphical communication. The class covers how we look at, process, and design data visualizations, incorporating attentive processing and Gestalt principles. I even get to bring in bits of data viz history—beginning with William Playfair in the 18th century—and discuss what postmodern architecture can teach us about what not to do in data graphics. It’s a fun time, and it often results in me being invited to present versions of it for research retreats or other campus training programs. It pushes people who are deeply immersed in data to critically consider how their design choices affect how different audiences perceive their findings.

A second, less expected way my humanistic lens has proved useful is in thinking critically about how we communicate about AI and data trustworthiness. I recently published an article that places generative AI in dialogue with the history of photography—a converging history, if you will—linking the two technologies through the lens of the Gartner Hype Cycle and human-machine collaboration in image-making. Counterintuitively—since many art historians I know are staunchly anti-AI—my training has pushed me toward a middle ground. It informs how I view generative AI in creative work, which in turn influences how I approach it as an information professional promoting ethical AI use, advancing campus-wide AI literacy, and developing AI-assisted solutions for libraries.

This perspective has allowed me to collaborate closely with colleagues in our Scientific Computing and Imaging (SCI) Institute, the One-U Responsible AI Initiative, and our Office of Research Integrity and Compliance. For example, we’ve been working on measuring the impact of researchers’ data sharing practices (good, bad, and absent) and promoting open science, which naturally led us into thinking about how good data management improves AI systems and whether we could establish a metric for how “trustworthy” a dataset is.

My biggest humanistic contribution to these discussions has been reframing what trustworthy even means. If it’s just the appearance of good data-sharing practices—ticking off the boxes to adhere to FAIR data principles—that’s not enough, especially if the data lacks a track record of being useful or reused in other contexts. Ultimately, something can look trustworthy without being so. To really trust a dataset, you need the full picture: robust documentation combined with evidence of usability and considerations of potential gaps and biases. In these spaces, I may not be the most technically proficient but being able to question the approach and encourage precision in how we write and talk about these topics is where I have an impact.

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