There's a goodbye email. A small send-off in the break room. Maybe a gift card.
And then they're gone, and so is everything they knew.
The code, dashboards and scripts are still there and are still running... for now. When an engineer leaves, so does their institutional knowledge and understanding: why the data is shaped the way it is, which logic was a quick fix that became permanent, what that one cryptic field in the enrollment table actually means.
In higher education, this scenario plays out more often than most institutions want to admit. And the consequences rarely announce themselves loudly. They creep in quietly: a report that doesn't match last year's, a dashboard executives stop trusting, lost funding due to poor reporting, student loss due to poor intervention, you can forget AI initiatives because without a reliable data foundation- it is meaningless.
Most institutional data warehouses weren’t built from a long-term student success strategy.
They were built from urgency.
A retention report was needed for the board.
Accreditation required outcome data.
Advising needed a list of students 5% from graduation
Each request added complexity. And over time, one engineer became the person who understood how it all worked.
That’s not unusual. It’s how reactive data strategy evolves.
But when institutional knowledge lives in one person’s head, student success becomes fragile. If that engineer left tomorrow, could your institution confidently track retention trends, measure advising impact, or timely identify at-risk students with consistency?
It’s not just reporting that suffers.
It’s the institution’s ability to understand — and improve — the student journey.
And when that foundation cracks, students feel the impact.
Higher education has a structural disadvantage when it comes to retaining top talent.
The compensation gap with the private sector is real and widening. But salary isn't the only issue. Many data engineers in higher ed describe a quieter kind of exhaustion: being the only person who truly understands how something works, fielding urgent requests that pull them away from real architectural work, and watching governance decisions get made without their input, and then getting handed the cleanup.
When that person leaves, institutions don't just lose technical capacity. They lose: the business logic that never made it into documentation; the governance decisions made verbally in a meeting two years ago; the context behind every "temporary" workaround that became load-bearing infrastructure; the institutional memory that no job posting can replace.
Hiring a replacement doesn't fix this. You can bring in someone just as skilled, and they'll spend YEARS reverse-engineering what the last person built. If they can.
Let's move past the IT conversation and focus on what really matters: Students.
Are your students getting the support they need, at the right moment, before it’s too late? When data architecture lives in one person's head rather than in documented, governed systems, the consequences don't stay in the server room. They show up in a student's trajectory.
Early Warning systems are useless with poor data. Advisors depend on data to identify students who are slipping before they disappear. But as systems evolve and upgrade, accuracy erodes quietly. The dashboards still load. The numbers just slowly stop reflecting reality, like data weeds taking over while everything looks fine from a distance. By the time someone notices, the student who needed intervention three weeks ago has already stopped showing up.
Trend analysis breaks down, and so does your ability to understand your students. Student success is longitudinal work. If the logic behind your data shifts every time personnel change, cohort comparisons fall apart. You can't identify what's actually moving the needle if your measuring stick keeps changing shape.
AI or Predictive Technologies Fail. Predictive tools are only as good as the data underneath them. If that foundation is fragile, your most promising student success initiatives stay theoretical, and students are the ones impacted.
The students who are already most vulnerable pay the highest price when data fails quietly.
The issue isn't whether you have talented engineers. You probably do.
The question is: does your data architecture depend on them to function, or just to improve?
There's a meaningful difference. A well-designed data foundation should be something any qualified person can step into and understand. Raw data preserved in its original form. Transformation logic documented and visible. Governance rules applied at a layer that can be audited and updated. Historical snapshots maintained consistently over time, regardless of who's managing the system.
It should not require a 90-minute onboarding call with a departing employee to understand why enrollment numbers look the way they do.
We've solved versions of this problem before.
Financial controls don't live in one person's spreadsheet. Accreditation compliance isn't stored in a single administrator's notebook. Enrollment operations run whether or not the person who built the process is still in the building.
We institutionalized those functions because we learned, sometimes painfully, what happens when we don't.
Data infrastructure deserves the same treatment. Not because the engineers are unreliable, but because all people eventually leave. Retirements. Promotions. Better offers. Life. The question is whether the institution's knowledge base survives the transition. Your data is the foundational identity for institutions.
Here's what institutions with durable data infrastructure have in common, and none of it requires a massive overhaul or a year-long project.
Raw data is preserved daily, before any transformation touches it. If something breaks or changes, you can always go back to the source.
Historical snapshots are maintained with consistent logic. Year-over-year comparisons actually mean something because the data was measured the same way each time.
Data lineage is documented inside the system, not inside someone's memory. Where data comes from, how it moves, and what happens to it along the way is visible to anyone who needs to know.
Governed data is kept separate from exploratory or analytical layers. Business rules can evolve without breaking everything downstream.
Most importantly, they treat data architecture as an essential foundation for the institution, not as someone's personal project.
The talent retention challenge in higher education isn’t going away.
But institutions can reduce their vulnerability by:
Designing architectures that preserve institutional memory
Prioritizing daily historical data retention (snapshots)
Making data lineage visible and governed
Reducing dependence on undocumented transformation logic
Building foundations that support long-term strategy—not just immediate reporting
Because the real question isn’t:
“What happens when a key engineer leaves?”
It’s:
Will your data still tell the truth when they do and how will you know if it doesn’t?
Let us help you reduce the institutional risk and create a future-proofed data foundation today.